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  1. July 10, 2026
    1. 🔗 benji.dog rss

      O: "Burj Khalifa is the tallest building, right?" Me: "yes" O: "So what's the shortest building?" Me: 😶

    2. 🔗 anthropics/claude-code v2.1.206 release

      What's changed

      • Added directory path suggestions to /cd, matching /add-dir behavior
      • Added a /doctor check that proposes trimming checked-in CLAUDE.md files by cutting content Claude could derive from the codebase
      • /commit-push-pr now auto-allows git push to the repo's configured push remote (remote.pushDefault, or the sole remote when only one is configured) in addition to origin
      • Gateway: /login now supports Anthropic-operated public gateway endpoints
      • EnterWorktree now asks for confirmation before entering a git worktree outside the project's .claude/worktrees/ directory
      • Background agents now upgrade to a new version in the background right after a Claude Code update, instead of paying a slow stale-session upgrade when you attach
      • Fixed an expired login failing every model with a misleading "There's an issue with the selected model" error instead of prompting to run /login
      • Fixed claude --resume and --continue not responding to keyboard input on startup
      • Fixed MCP servers configured via --mcp-config or .mcp.json ignoring a per-server request_timeout_ms, which caused long-running MCP tool calls to time out at the 60s default in fresh sessions
      • Fixed CLAUDE_CODE_EXTRA_BODY being silently ignored by claude agents / --bg background workers; the shell-exported override now follows the dispatching session
      • Fixed OAuth MCP servers requiring manual re-authentication after a single failed token refresh
      • Fixed --permission-prompt-tool pointing at an MCP server crashing with "MCP tool not found" on cold start before the server finishes connecting
      • Fixed /model picker rows printing a price for a different model than the row named, and stopped quoting first-party list prices on providers that don't bill them
      • Fixed server-provided model rows being misplaced in the /model picker when an entitlement or allowlist restriction drops the row they were positioned against
      • Fixed desktop sessions getting stuck showing "running" after a slash command was sent mid-turn
      • Fixed keyboard input being ignored in the agents view when a setup prompt appeared before a bare claude --resume on Windows
      • Fixed claude rm leaving the removed job in the daemon roster, causing the row to reappear in claude agents
      • Fixed /remote-control showing "Unknown command" when logged out — it now explains how to sign in
      • Fixed left arrow not stepping back out of a phase or agent in the workflow detail view
      • Fixed /status listing the same broken-install warning twice
      • Fixed false "disused plugin" tips and skewed disuse telemetry for LSP plugins
      • Fixed /doctor's update check to compare Homebrew installs against their cask's channel instead of the settings channel
      • Fixed the fullscreen jump-to-bottom pill suggesting Ctrl+End on macOS, not showing rebound chords, and wrapping over the transcript
      • Bedrock: fixed a multi-minute startup hang when using an awsCredentialExport helper on networks with restricted egress
      • Improved /code-review findings quality on claude-opus-4-8 across all effort levels
      • Improved agents view: status column now uses full terminal width instead of truncating at 64 characters
      • Changed agents view: Ctrl+X now permanently removes a completed session, and sessions no longer render twice; deleted background jobs stay deleted
    3. 🔗 Barre/ZeroFS v2.0.8 release

      What's Changed

      • Add deterministic simulation testing for the extent-store data plane by @Barre in #519
      • Fuzz and harden the 9P and NBD protocol decoders by @Barre in #521
      • Fail closed on segment materialization HEAD errors by @Barre in #522
      • Backpressure extent writes before growing an overdue open segment by @Barre in #520

      Full Changelog : v2.0.7...v2.0.8

    4. 🔗 New Music Releases Bring Me the Horizon - Count Your Blessings | Repented rss

      Bring Me the Horizon - a new release is available:

      • 2026-07-10: Count Your Blessings | Repented (Album)

      Amazon: Canada | Deutschland | France | United Kingdom | United States

      Visit muspy for more information.

    5. 🔗 New Music Releases If These Trees Could Talk - The Hidden Hand rss

      If These Trees Could Talk - a new release is available:

      • 2026-07-10: The Hidden Hand (Album)

      Amazon: Canada | Deutschland | France | United Kingdom | United States

      Visit muspy for more information.

  2. July 09, 2026
    1. 🔗 r/LocalLLaMA GLM-5.2 (744B MoE) on a 25GB-RAM consumer machine rss

      GLM-5.2 (744B MoE) on a 25GB-RAM consumer machine | submitted by /u/yogthos
      [link] [comments]
      ---|---

    2. 🔗 Simon Willison The new GPT-5.6 family: Luna, Terra, Sol rss

      OpenAI's latest flagship model hit general availability this morning, and comes in three sizes: Luna, Terra, and Sol (from smallest to largest).

      The new models are priced per 1M input/output tokens as Luna $1/$6, Terra $2.50/$15, Sol $5/$30. For comparison, the Claude Opus series are $5/$25 and the Claude Fable 5 is $10/$50, but price-per-million tokens doesn't tell us much now that the number of reasoning tokens can differ so much between models for the same task.

      All three models have a February 16th 2026 knowledge cutoff, a million token context window, and 128,000 maximum output tokens.

      OpenAI's biggest benchmark claim concerns long-running agentic performance, with one benchmark showing all three models outperforming Claude Fable 5:

      We trained GPT-5.6 to get more useful work from every token. On Agents’ Last Exam, an evaluation of long-running professional workflows across 55 fields, GPT-5.6 Sol sets a new high of 53.6, eclipsing Claude Fable 5 (adaptive reasoning) by 13.1 points. Even at medium reasoning, it beats Fable 5 by 11.4 points at roughly one-quarter the estimated cost. That efficiency extends to smaller models, which are essential to making intelligence more abundant and affordable: GPT-5.6 Terra and GPT-5.6 Luna outperform Fable 5 at around one-sixteenth the cost.

      Amusingly, one self-reported benchmark that Fable 5 crushed the GPT-5.6 family on was SWE-Bench Pro, where Fable 5 got 80% compared to GPT-5.6 Sol getting 64.6%. This may help explain why OpenAI chose to publish this article yesterday specifically calling out SWE-Bench Pro for problems they found while auditing that benchmark:

      In light of these results, we estimate that ~30% of SWE-bench Pro tasks are broken, and advise that model developers carefully examine results

      I've had some early access to GPT-5.6 Sol - it's definitely very competent, though so far it hasn't struck me as better than Fable at the kind of complex coding tasks I've been using with Anthropic's model.

      As usual, the model guidance for using GPT-5.6 has the most interesting details. There are a bunch of new API features that I need to explore (and probably add support for in LLM), including:

      • Programmatic Tool Calling allows the models to "compose and run JavaScript that orchestrates tool calls" - which sounds to me like it could help bridge the gap between MCPs and full terminal sessions that can compose CLI utilities in useful ways. Also reminiscent of the dynamic filtering mechanism Anthropic added to their web search tool, which allows code execution against web results as part of a single model turn.
      • Multi-agent lets the model "spin up subagents for parallel, focused work" - the sub-agent pattern now baked into the core API.
      • Prompt cache breakpoints brings the Claude model of prompt caching to OpenAI, letting you be explicit about where the cache breakpoints are rather than relying on the API to detect them automatically. Personally I much prefer automatic detection (still supported by OpenAI), but presumably there are optimization cost savings to be had here if you put the work in.
      • You can now set detail: original on image requests to avoid resizing the image at all before it is processed.

      Here's a full page with 18 different pelicans - for reasoning efforts none, low, medium, high, xhigh, and max across the three different models. It also lists their token and calculated costs - the least expensive was gpt-5.6-luna at effort none for 0.71 cents, the most expensive was gpt-5.6-sol at max reasoning level for 48.55 cents.

      A grid of nine pelicans riding bicycles, of varying quality

      In further pelican news, if you jump to 17:50 in their livestream from this morning you'll see OpenAI's own demo of 3D pelicans riding a tricycle, a bicycle, a pony, and another pelican!

      Frame from a livestream showing a 3D model of a pelican riding another pelican

      You are only seeing the long-form articles from my blog. Subscribe to /atom/everything/ to get all of my posts, or take a look at my other subscription options.

    3. 🔗 The Pragmatic Engineer The Pulse: Interesting AI coding stats from Cursor rss

      Hi, this is Gergely with a bonus, free issue of the Pragmatic Engineer Newsletter. In every issue, I cover Big Tech and startups through the lens of senior engineers and engineering leaders. Today, we cover one out of four topics _a past _The Pulse issue__ . Full subscribers received the article below five weeks ago. If you 've been forwarded this email, you can subscribe here .

      Cursor has just released a new report based on two years of its aggregated usage data, and there are some interesting findings:

      Power users generate 10x as many lines of code vs the median

      alt Source:Cursor

      The median dev using Cursor (the p50) generates about 700 lines of code per week with it, while for the 90th percentile, it's closer to 9,000 lines.

      Top 1% of users create incredible volume of code

      The p99 data is pretty stunning:

      altThe top 1% of Cursor users (p99) vs the top 10% (p90)

      The top 1% of users generate around 30-40K lines of code per week! That's the equivalent of what ~45 "median" devs generate in the same period.

      It's worth asking how these top 1% of users are different. Are they writing a lot more greenfield code, do they have a bias for not using libraries, are they tokenmaxxing to get to the top of leaderboards? Do they generate 45x as many bugs, and importantly: are they adding a lot of business value with the software they ship?

      Cursor consumes 10x more input tokens than it generates in output tokens

      This is surprising: 90% of Cursor's token usage is input tokens! This means that most of the tokens used are for reading the existing codebase and documentation. Outputting of code is a minority usage:

      altInput tokens (Cursor reading the codebase) is the bulk of token usage

      In some ways, this usage makes sense: as devs, we always spent far more time on reading the code, compared to lines of code we typed out. The "10:1 read- to-write" ratio is a classic. Here's Robert. C. Martin (aka "uncle Bob") sharing this observation in 2008, in his book, Clean Code:

      "Indeed, the ratio of time spent reading versus writing is well over 10 to 1. We are constantly reading old code as part of the effort to write new code… [Therefore] making it easy to read, makes it easier to write."

      I find it amusing that we're now seeing this 10:1 read / write ratio for token usage with AI agents!

      Input tokens become the main AI token cost

      Input tokens are priced at a fraction of output tokens: for example, Opus 4.7 charges 5x more for output tokens than for input tokens ($5 per 1 million input tokens and $25 per 1 million output tokens). Still, thanks to input tokens dominating token usage, Cursor is seeing input tokens account for closer to 70% of the cost of AI coding agents:

      altInput tokens dominate Cursor costs

      Without caching context, token cost would be 10x higher

      Cursor does smart caching of context, to avoid re-generating old context with more new input tokens. When taking cache usage into account, Cursor only spends 0.6% of tokens on output tokens. The remaining 99% is split between cache read (90%), cache write (2.5%), and input tokens (7%):

      altOutput tokens are only 0.6% of token usage when considering cache reads & writes

      I wonder if context reuse and caching will be a key AI efficiency component in the future? AI tokens are expensive to generate, so any form of reuse will make a lot of sense, especially in workflows like coding where a lot of existing context is reused.

      Of course, Cursor sharing this detail also makes sense, as they remind everyone that building an efficient AI agent harness is far from trivial. Indeed, if you roll your own agent harness, you also need to put an efficient caching layer in place to match the efficiency of tools like Cursor.

      Opus is the most expensive model & could hurt Anthropic

      At the time of publishing, Opus 4.7 was still considered the most capable coding model. However, it's also very expensive, and Cursor's own data shows it's close to 10x more expensive than its own Composer 2.5 model:

      altOpus 4.7 is twice as expensive as GPT-5.5 & nearly 10x more than Composer 2.5

      It's significant that Cursor compares the cost of a single agent request; it's not a direct token-to-token comparison. And it's worth noting this benchmark is being shared by Cursor, which has an incentive for its Composer model to appear the lowest-cost.

      Still, assuming you can get similar-enough results with a 10x cheaper model, it is a saving that's hard to ignore, especially for mid-sized and above companies. I would not be surprised if more tech companies find ways for devs to use less capable - but cheaper - models for less critical work.

      More expensive models result in higher acceptance rates

      An interesting metric Cursor shares is cost-per-line-added, per model:

      alt

      This metric is a more realistic cost because it correlates to output: "smart" models that are expensive, but which produce code that is frequently accepted, are penalized by the cost-per-agent-request metric, but they're not here.

      Indeed, Opus 4.7 has the same cost-per-line-accepted as GPT 5.5 at half the cost per agent request. In this comparison, Cursor's Composer model is "only" 5x as efficient.

      Missing from both lists are Google's Gemini models, a strange omission by Cursor. I reached out to Cursor and they told me that Gemini was left out simply because they see very little usage of this model on their platform, similar to the sparsely used Grok model.

      Almost half of AI changes accepted without manual review by devs

      I've left the most interesting part of this report to last: in just a month, among devs using Cursor, it has gone from 10% who let AI agents create commits without a manual step, to around 40% of devs who no longer personally check the code:

      alt

      The jump correlates with Opus 4.7 and GPT-5.5 being released, and around the time when many devs seem to have concluded that writing code by hand is dying after experiencing this generation of models' capability at generating code.

      Check out the full report from Cursor for more details. Thanks to the team for releasing this data!

      Read the full issue of The Pulse this excerpt is from, or check out the latest The Pulse from today. Today's issue covers:

      • Bun's Rust rewrite with Fable: what can we learn?
      • Anthropic's Fable, OpenAI's GPT-5.6 Sol, Cursor's Grok 4.5, Meta's Muse
      • North Korean hackers keep trying to infiltrate full-remote companies
      • Industry Pulse: Meta's key logging exposed sensitive data, massive cuts at Xbox, Meta could not buy enough AI capacity from Google, Qualcomm acquires Modular, and memory price hikes hit Apple products.
    4. 🔗 pydantic/pydantic-ai-harness v0.6.0 (2027-07-09) release

      What's Changed

      • Bump vcrpy to 8.2.1 to fix YAML deserialization RCE by @adtyavrdhn in #300
      • feat(subagents): configurable delegate-tool retries by @dsfaccini in #324
      • feat(subagents): contain unexpected sub-agent crashes as bounded retries by @dsfaccini in #326
      • feat(experimental): serve a Pydantic AI agent over ACP by @adtyavrdhn in #274
      • feat(experimental): Dynamic Workflows Capability by @adtyavrdhn in #273

      Full Changelog : v0.5.0...v0.6.0

    5. 🔗 @malcat@infosec.exchange In the upcoming 0.9.15 release, Malcat will embed its own 100% native mastodon

      In the upcoming 0.9.15 release, Malcat will embed its own 100% native #capa engine. So you get:
      - x10 .. up to x100 scan speedup
      - command line tool (using headless malcat lib)
      - embedded capa rule editor
      - additional architectures: arm, mips and even ... python:

    6. 🔗 navidrome/navidrome v0.63.1 release

      Changelog

      Bug fixes

      • 052f10f: fix(build): prevent 32-bit startup crash (segfault/SIGILL) in downloads binaries (#5739) (@deluan)
      • f48943c: fix(plugins): discard buffered scrobbles when a plugin is removed (#5737) (@deluan)
      • 4652b46: fix(plugins): populate username for buffered plugin scrobbles (#5736) (@deluan)
      • 42d4363: fix(service): rewrite systemd service template for kardianos/service v1.3.0 (#5743) (@deluan)

      Full Changelog : v0.63.0...v0.63.1

      Helping out

      This release is only possible thanks to the support of some awesome people!

      Want to be one of them?
      You can sponsor, pay me a Ko- fi, or contribute with code.

      Where to go next?

    7. 🔗 Barre/ZeroFS v2.0.7 release

      Segment reclaim & compaction correctness

      • 169d1d4 : Compare the full FrameLoc in the compaction repoint CAS so a rewrite into the same source segment is not reverted to the stale frame.
      • b9d0dd8 : Read the segment-reclaim segcount scan at the durable level so a segment is never deleted while its death is still an unflushed in-memory debit.
      • 7e9e4e2 : Prove reclaim deletes from the durable view under the WAL-off production config.
      • eb4f9e7 : Check the durable view too in the segment directory-verify so an unflushed overwrite cannot mask a durably referenced frame from the delete backstop.
      • 9a3827c : Keep compaction's gather compressed end to end (verify and AAD-rebind only), cap the round in stored bytes plus per-frame overhead, and fan the batch AEAD out on rayon in both directions.

      HA / replication correctness

      • dcb1e6c : Key the cross-term tail clear on the tail's own epoch, not the heartbeat-advanced fence, so a restarted leader's stale tail cannot replay over the new term's fsync-acked writes.
      • cfae108 : Validate takeover replay against a per-batch durable provenance stamp and gate boot on a latest-leader record so neither a stale tail nor an election from silence can regress acked state.
      • b3b6028 : Raise the replication decode limit above tonic's 4 MiB default.

      Robustness fixes

      • f2d5a6b : Forward mid-scan iterator errors into the Db::scan stream instead of swallowing them as a clean end-of-range.
      • 5b66e77 : Reject a write or trim whose offset + length overflows u64 as EINVAL instead of wrapping into a request-task panic or a stray unreachable extent.

      Refactors & housekeeping

      • 2bdf0e0 : Fix clippy warning.
      • 0cbf4b3 : Split extent.rs into an extent/ module: read, write, select, reclaim, compact.
      • f67f347 : Split fs/mod.rs into boot, handle, and per-op ops/ files with their tests.

      Full Changelog : v2.0.6...v2.0.7

    8. 🔗 r/LocalLLaMA GLM-5.2 fearmongering in the press rss

      I don't know where this is headed, but I don't like it.

      https://futurism.com/artificial-intelligence/open-source-ai-model-scary- mythos

      GLM-5.2 can be downloaded by anybody, can be run on virtually any hardware, and unlike Mythos or Fable, there’s no vendor playing the middle man between the AI models and the users, raising the cybersecurity stakes considerably.

      Put simply, while these frontier models can aid researchers in patching holes in commonly used software, the can also be abused by hackers to bypass existing defenses.

      Security firms Semgrep and Graphistry both found that GLM-5.2 was proficient at identifying software bugs and performing other cybersecurity tasks. “We Have Mythos at Home,” Semgrep titled its benchmarking.

      Hopefully this fearmongering won't be used to justify censorship, but we live in strange days. I don't know what to expect anymore.

      submitted by /u/ttkciar
      [link] [comments]

    9. 🔗 r/LocalLLaMA Now brothers we know why we are so fucked up rss

      Samsung chip division's single-year profits beat its past 40 years of

      profits, combined, due to increased memory and storage prices — Samsung passes Nvidia to become most profitable company in the world, notches 19x quarterly increase in profit

      https://www.tomshardware.com/tech-industry/samsungs-chip-division-expects-to- out-earn-its-entire-40-year-history-in-2026

      submitted by /u/perelmanych
      [link] [comments]

    10. 🔗 Rust Blog Announcing Rust 1.97.0 rss

      The Rust team is happy to announce a new version of Rust, 1.97.0. Rust is a programming language empowering everyone to build reliable and efficient software.

      If you have a previous version of Rust installed via rustup, you can get 1.97.0 with:

      $ rustup update stable

      If you don't have it already, you can get rustup from the appropriate page on our website, and check out the detailed release notes for 1.97.0.

      If you'd like to help us out by testing future releases, you might consider updating locally to use the beta channel (rustup default beta) or the nightly channel (rustup default nightly). Please report any bugs you might come across!

      What's in 1.97.0 stable

      Symbol mangling v0 enabled by default

      When Rust is compiled into object files and binaries, each item (functions, statics, etc) must have a globally unique "symbol" identifying it. To avoid conflicts when linking together different Rust programs, Rust mangles the original name of items to include additional context such as the module path, defining crate, generics, and more. Historically, this mangling was based on the Itanium ABI, also (sometimes) used by C++.

      The new mangling scheme resolves a number of drawbacks from the previous one:

      • Generic parameter instantiations preserve their values, rather than being tracked solely behind a hash
      • Inconsistencies: not all parts used the Itanium ABI, meaning that custom demangling was still necessary

      Since Rust 1.59, the compiler has supported opting into a Rust-specific mangling scheme via -Csymbol-mangling-version=v0. Since November 2025, this scheme has been enabled by default on nightly, and 1.97 is now enabling it on stable Rust. The legacy mangling scheme can only be enabled on nightly, and the current plan is to fully remove it.

      See the previous blog post for more details.

      Cargo support for denying warnings

      It's common practice to deny warnings in CI. Historically, doing so is typically done through RUSTFLAGS=-Dwarnings. With Rust 1.97, Cargo controls how warnings interact with build success: either silencing them (via allow level), rendering without failing (default, warn), or denying them (via deny).

      As a result of Cargo configuration determining the behavior, using this feature doesn't invalidate the underlying build cache, meaning that it's easy to temporarily opt-in. For example, if warnings are adding unwanted noise while working through fixing errors after a refactor, you can run CARGO_BUILD_WARNINGS=allow cargo check, temporarily silencing them.

      In CI, jobs can instead set CARGO_BUILD_WARNINGS=deny to deny warnings. This can be combined with --keep-going to collect all errors and warnings rather than stopping on the first failing package.

      See the documentation for more details.

      Linker output no longer hidden by default

      rustc invokes a linker on behalf of users. Historically, rustc has silenced linker output by default if the link completes successfully. This can mask real problems, though, so in Rust 1.97 we are enabling linker messages by default. These are emitted as a warning lint, for example:

      warning: linker stderr: ignoring deprecated linker optimization setting '1'
  |
  = note: `#[warn(linker_messages)]` on by default

      Common linker messages that have been diagnosed as false positives or intentional behavior are filtered out by rustc. Several defects have already been fixed as a result of no longer hiding this output on nightly.

      Note that currently, linker_messages is a special lint that is not affected by the warnings lint group. This is intentional as rustc generally doesn't control linker output as precisely, and it's not uncommon for output to only appear on some platforms. If you are seeing what you think is a false positive output from the linker, please file an issue.

      To silence the warning in the mean time, you can configure the lint level to allow. This can be done through Cargo.toml by adding a lints section like this:

      [lints.rust]
linker_messages = "allow"

      Stabilized APIs

      These previously stable APIs are now stable in const contexts:

      Other changes

      Check out everything that changed in Rust, Cargo, and Clippy.

      Contributors to 1.97.0

      Many people came together to create Rust 1.97.0. We couldn't have done it without all of you. Thanks!

    11. 🔗 Console.dev newsletter Davit rss

      Description: Native macOS Container GUI.

      What we like: Manage Apple Containers via the UI. Open a shell inside any container. Supports volumes, images, networks. Built-in file browser. View container stats and inspect container details. Native macOS app, not Electron.

      What we dislike: Not a Docker replacement - it’s Apple’s own implementation. Try OrbStack if you need that (and other things like VMs).

    12. 🔗 Console.dev newsletter ZeroFS rss

      Description: Log-structured filesystem for S3.

      What we like: Makes S3-compatible buckets appear as POSIX filesystems or raw block devices. Supports NFS, 9P, NBD. Use ZFS to mirror across regions. Segments are immutable, compressed, encrypted. Local caching. Optional web dashboard and file manager.

      What we dislike: AGPL licensed by default, alternative available on a commercial basis.

    13. 🔗 Ampcode News The Dial rss

      Amp's agent modes are now a dial: low, medium, high, ultra. They replace smart, deep, rush, and large.

      Amp's mode dial in the CLI showing low, medium, high, and ultra

      The old modes were models in disguise: each name hid a model, a prompt, a reasoning effort — and to pick one, you had to know what that model was like this month. That world is gone. The models converged, open-weight models got seriously good, and the only question left is capability against cost.

      The dial asks one question: how hard is this task?

      Missing in either direction costs you. Undershoot and the model churns: wrong fix, re-prompt, wrong fix again. You pay three times for a result you could have had once. Overshoot and you're using Fable to fix a typo. Set it right and you pay for exactly the intelligence the task needs.

      • ultra — The outcome is clear, but the path is full of unknowns. Migrations, architecture, changes that span many files, systems, and decisions the model has to discover as it goes.
      • high — You know where the change goes, but getting it right is hard: cross-cutting changes, concurrency, bugs where a subtle miss is expensive. You get diffs closer to reviewer-ready than medium gets you — but plan on one round of feedback before merging, and about twice the wait.
      • medium — You know roughly what you want. This should be your default. It handles messy, multi-part tasks, fuzzy requirements, the steps you didn't spell out. Strong enough for most work, fast enough to steer.
      • low — You know exactly what you want. Bug fixes, tests, refactors, features you can describe precisely. There is less to figure out for the model, so low builds it.

      Turn the dial with Ctrl+S in the CLI, or with the mode picker in the web app.

      Amp's mode picker in the web app showing low, medium, high, and ultra

      Under the Hood

      We want you to know exactly what you're getting, so here's what backs each mode today. This wiring will change as models improve. The dial won't.

      • ultra: Claude Fable 5, with a system prompt written for it. GPT-5.5 as the oracle.
      • high: GPT-5.5 at xhigh reasoning effort. Claude Fable 5 as the oracle.
      • medium: GPT-5.5 at medium reasoning effort. GPT-5.5 at high effort as the oracle.
      • low: GLM-5.2, Z.ai's open-weight model, the strongest open model on agentic coding. GPT-5.5 as the oracle. (Workspace admins can choose to use GPT-5.5 low instead of GLM-5.2 here.)

      Reasoning effort is part of the tier now. No more cycling Opt+D through effort levels on top of picking a mode.

      Every mode has an oracle for second opinions. On the top tiers, it's the other frontier model: in high, GPT-5.5 writes and Fable reviews. In ultra, Fable writes and GPT-5.5 reviews.

      Migrating

      • smart, deep → medium (same model and effort as deep). Turn up for hard problems.
      • rush → low.
      • deep**3 -> ultra or high

      Want to Tune It Yourself?

      The dial removes knobs from the default experience, not from Amp. Plugins can register their own agent modes with your model, your prompt, and your tools, and they show up right next to the built-in ones.

      We used that same plugin API to package up the deprecated modes — exact system prompts, exact tool lists, same models and reasoning efforts. If you want smart, deep, rush, or large back, install them:

      amp plugins add --auto-update @amp/smart-classic
amp plugins add --auto-update @amp/deep-classic
amp plugins add --auto-update @amp/rush-classic
amp plugins add --auto-update @amp/large-classic

      Then run plugins: reload (or restart the CLI) and they appear in the mode picker as Smart (classic), Deep (classic), Rush (classic), and Large (classic) — the original names stay reserved for the built-ins. --auto-update keeps them current when we update the plugins; drop it if you'd rather pin. The full list of installable modes is on ampcode.com/models.

      Start at medium. Turn it down when the task is clear. Turn it up when a miss costs more than the wait.

      We'll follow up with posts on each mode and numbers on what each one can handle.

  3. July 08, 2026
    1. 🔗 IDA Plugin Updates IDA Plugin Updates on 2026-07-08 rss

      IDA Plugin Updates on 2026-07-08

      New Releases:

      Activity:

      • atelier
        • e98c4662: chore: bump landing submodule - footer reorganized, all 13 vs-compar…
        • 7ff16e86: docs: add vs-caveman and vs-rtk comparisons; expand README header nav…
        • 9357c0f9: docs: expose full benchmark suite + named-competitor comparisons
        • f3b89287: chore: bump to v0.2.7
        • 7c30a33e: chore: bump to v0.2.6
        • f5e2a0c2: Revise landing page copy and mockup for Atelier to emphasize savings …
        • d2254599: Move installer state under atelier home
      • ghidra
        • ee4fba70: Merge remote-tracking branch 'origin/GP-0_d-millar_test_fix'
        • 28f58b3e: GP-0: Javadoc fixes
        • 8f1fd58f: GP-0: tired of seeing this
        • de2b2180: Merge remote-tracking branch 'origin/patch'
        • f9316e1e: Merge remote-tracking branch
      • ida-multi-mcp
        • 9016453f: docs: update star history chart embed URL (#25)
        • 7b99f0c7: fix: real batched jTrans embedding + MPS device support (#23)
        • 58dfe7bc: feat: serve partial similar_functions() results during index builds (…
      • ida_rpc
      • plugin-ida
        • 7ace61d3: Merge pull request #155 from RevEngAI/feat-PRO-313
        • 3f650955: feat(PRO-313): analysis tags
        • 620a5bd7: Merge pull request #154 from RevEngAI/feat-PLU-311
        • 73ee01a7: fix: suppress error message - race between queued delivery and teardown
        • ce5a4614: feat(PLU-311): persist function jumps in chat history, render bug fix
        • 8aa03a2e: Merge pull request #153 from RevEngAI/chore/auto-bump-revengai
        • f253a28d: chore: bump revengai to 3.110.0
      • project
        • a1ac69c6: added ghidra.sh, demangle.py and CLI flag -ghidra
      • Spectra
        • 2000745d: Improve spawn_subagent UI; fix Popen pipes
        • a915630a: Load shell_auto_approve_limit from config
        • c44cf8fe: Display subagent events in shell REPL
        • 265adde9: Print notice when auto-approving safe shell commands
        • 14224c15: Make shell auto-approve limit configurable
        • acdd604c: Track shell approval state and handle input aborts
        • cb9493b5: Add safety checks and thread safety to shell commands
        • 2bfdd2af: Add SSH remote execution and file transfer tools
        • e0bb4348: Refine shell command safety patterns
        • 3cec041d: Document new interactive CLI features
        • 8d20b025: Add graceful Ctrl+C handling for agent interruption
        • 7812b44c: Print full shell command in approval messages
        • be9dd0ec: Show truncated shell command in approval messages
    2. 🔗 anthropics/claude-code v2.1.205 release

      What's changed

      • Added an auto mode rule that blocks tampering with session transcript files
      • Fixed --json-schema silently producing unstructured output when the schema was invalid, and schemas using the format keyword being rejected
      • Fixed a message sent while Claude was working being silently lost when the turn ended at the --max-turns limit
      • Fixed Windows worktree removal deleting files outside the worktree when an NTFS junction or directory symlink existed inside it
      • Fixed background agents staying shown as "failed" or "completed" in the agent list after being resumed with SendMessage
      • Fixed background jobs flipping from "needs input" back to "working" in the agent list when the agent's turn contained no readable text
      • Fixed claude attach erroring when a background agent was mid-upgrade restart instead of waiting for it to come back
      • Fixed session-to-PR linking missing a PR created in a Bash call whose output exceeded the 30K inline limit
      • Fixed claude mcp add-from-claude-desktop getting stuck when a server name contains unsupported characters; invalid names are now reported and remaining servers still import
      • Fixed a plugin LSP server that fails to initialize preventing a valid LSP server from another plugin handling the same file extension
      • Fixed a Windows crash when the directory Claude was launched from is deleted, locked, or unmounted while a command is running
      • Fixed a crash when a file watcher was closed while a directory scan was still in flight
      • Fixed project verify skills being rewritten on every session instead of only when a documented command changed
      • Fixed the agent view rendering one line too high and clipping its header when the job list slightly overflowed the screen
      • Fixed background tasks in the web and mobile Remote Control panels showing stale "Running" status by forwarding full task state on every membership change
      • Improved auto mode to ask before running rm -rf on a variable it can't resolve from context
      • Auto-update binary downloads now stream to disk instead of buffering in memory, cutting the updater's peak memory usage by roughly 400 MB
      • Background task notifications now explicitly state that no human input has occurred, preventing fabricated in-transcript approvals from being acted on
      • Improved agent view: sessions that edit, merge, comment on, or push to an existing PR now link it in claude agents
      • Improved agent view: rows now show a colored state word and a classifier-written headline instead of raw tool call text, and the peek opens with full status including the exact ask for blocked sessions
      • /doctor is now a full setup checkup that can diagnose and fix issues; /checkup is its alias
      • Reserved the "Claude Browser" MCP server name (alongside "Claude Preview") ahead of the Claude Desktop pane rename; user-configured MCP servers can no longer register under either name
      • Fixed Cowork VM-mode local-agent sessions failing to start with "Not logged in · Please run /login" on CLI 2.1.203+
    3. 🔗 r/LocalLLaMA What China Said at the UN’s First Global Dialogue on AI Governance rss

      What China Said at the UN’s First Global Dialogue on AI Governance | Open source AI is a shared asset for all humanity. Chinese open source models such as DeepSeek and Qwen have significantly lowered the barriers and costs of AI adoption. China is committed to further promoting open source AI for industry, academia and research institutions, encouraging innovation, AI empowerment and an inclusive ecosystem through international cooperation, thereby injecting sustained momentum into AI development. submitted by /u/jld1532
      [link] [comments]
      ---|---

    4. 🔗 r/LocalLLaMA AI has completely revolutionized how I play RPGs rss

      Crossposting this here because I thought you guys might appreciate it.

      When ChatGPT and other open source LLMs first came out, there was a lot of speculation as to how these technologies could change gaming. I recall there being posts and comments about when we could have AI powered NPCs. Nvidia showcased ACE back in 2024, which was an NPC powered by a cloud LLM server. Fast forward to today, and there's a lot of doom and gloom around AI, rightfully so in the case of pretty much every closed source company. But on the bright side, open weight LLMs have advanced so much to the point where they are really good if you know exactly how to use them.

      Case in point: Skyrim. Skyrim afaik was one of the first test beds for integrating LLMs into video game NPCs thanks to its moddable nature and versatile fantasy setting. The first mod to come out was Mantella. While it was fairly barebones, it was a good proof-of-concept for how LLMs could be used to power conversations with NPCs. Then came the Herika mod, which was an individual NPC named Herika who was powered by an LLM. It expanded the abilities of the LLMs by allowing it to see NPC actions, dialogue, world events, etc, making the AIs smarter with more context. The devs of Herika then expanded the functionality to all NPCs and renamed the mod "AI Follower Framework" before then changing the name again to "CHIM" (a reference to some metaphysical shenanigans in The Elder Scrolls lore). I played with CHIM a lot before then migrating over to another LLM mod called SkyrimNet. It does much of the same thing as CHIM, but in my opinion its UI and controls are a lot more user friendly.

      Having finished creating a 500+ mods custom modlist built specifically for LLM gameplay and then playing with SkyrimNet for the last ~40 hours, I don't think I can ever play RPGs normally again. The amount of emergent storytelling that can be told with this tool is astounding IF you know its limitations and how to use it properly. Before using LLMs, I used to download a litany of quest mods and custom follower mods to get new experiences in Skyrim. Unfortunately, the quality of such mods can be hit or miss. The Rigmor Series of mods adds a new NPC named Rigmor who has her own backstory and a very in depth quest, but the writing strips away pretty much all character agency. The Interesting NPCs mod is another big one that adds a lot of characters with depth, but holy moly those NPCs get very soap-boxy and overly philosophical. SkyrimNet has been the perfect solution for this at least for me.

      With SkyrimNet, no longer do I have to download a morbillion NPC and quest mods. This singular mod allows me to create NPC personalities and actually role play with them. (Crazy, I know. Roleplaying in a Role Playing Game). If you're creative, willing to tinker with the system, and willing to accept a little jank, you can roleplay your own entire questlines.

      For example, in the vanilla Skyrim game, there's an NPC named Ranmir who's depressed because he thinks his wife Isabelle left him. When you investigate her disappearance, you find her dead in a cave. You then report her death to Ranmir, he gets the closure he wants, and then that's the end of the game.

      But for my character, I'm roleplaying as a Necromancer, and I had just recently obtained the Dead Thrall spell from the College of Winterhold. So instead of just letting Isabelle's corpse go to waste, I decided to turn her into a Dead Thrall, and I powered her intelligence using an LLM. In TES, necromancy is theororized to work by conjuring a daedra from Oblivion and placing its soul into the corpse of a mortal. For this RP, I made a backstory for the summoned daedra and named her Volla. This Volla was weak, timid, fearful, but filled with wanderlust for Tamriel. Having found possession of a new body in Isabelle, she journeyed alongside my necromancer and became a powerful warrior in her own right. However, the weakness of her will allowed the original mind of Isabelle to begin taking control of Isabelle's body again, threatening to erase Volla from Tamriel. But Volla's possession of Isabelle's body also threatened to erase Isabelle. Through a lot of RP and character development, Volla and Isabelle learned to coexist, eventually merging into one persona that is both and neither Volla nor Isabelle. Without getting further into my bad fanfiction, this entire questline was produced emergently with the use of an LLM in real time gameplay.

      This is just one of many examples I've had in my playthrough so far, and I imagine that there are many, many more to come.

      So, those are the pros, now here are the cons. The default parameters for SkyrimNet, CHIM, and LLMs mean that you have to handhold the AI a lot if you have a set story and character arc that you want to go through for a story. The LLM can't read your mind after all and will often default to generic storytelling. My story with Isabelle and Volla never would've happened if I hadn't directly injected character actions and dialogue into the prompt. The LLM really only produces what your creativity can imagine. It won't be super creative on its own.

      If you want good quality and fast NPC responses, prepare to subscribe to OpenRouter or another LLM service. I avoid using closed weight models like ChatGPT or Gemini for their pricing and my overall distaste with their business models. I've been using two open weight models for my RP: Google Gemma 4 31B for NPC dialogue and Deepseek V3.2 0324 for function calling. You might be able to run Gemma 4 31B on a high end workstation GPU, like an Nvidia RTX Pro 6000, at high speeds, but you certainly won't be able to run Deepseek V3.2. At 685 billion parameters, you would need a dedicated datacenter in your home to run it locally. As a result, the most financially sensible option is to just charge up an OpenRouter account with a few dollars and connect SkyrimNet to your OpenRouter token. Then you have to connect SkyrimNet to a Text-To-Speech engine, which isn't all that hard to run if you have an extra Nvidia-powered device laying around (an old 8GB VRAM gaming laptop in my case). Responses have been really fast and haven't hindered RP at all, but this set up can either require huge compute or require a subscription service.

      Finally, you really have to have a tinkerer's mindset to have a good experience right now. If you're the kind of person who dabbles in Linux command line shenanigans or enjoys compiling obscure software from GitHub repos, you won't have any problems modding Skyrim for use with LLMs. But for 99% of gamers, this kind of set up is very, very technical, and it certainly won't be for you. At least not yet.

      As the quality of smaller, local LLMs improves and the technology gets better, I can see SkyrimNet become more and more seamless for casual users. It's my hope that this kind of technology finds use in games that prioritize emergent storytelling. I can understand why most gamers would avoid this kind of technology in favor of hand-crafted, artisanal storytelling like those found in narrative-heavy games like Kingdom Come Deliverance, Cyberpunk, or God of War. But if you want to tell your own stories and have AI produce the special moments with NPC dialogue, then this tech is right for you.

      I already have 3000 hours in Skyrim over the past 10 years. 200 from vanilla and 2800 from modded. I intended originally to sustain my next couple hundred hours of gameplay just with the banger mods that are released on a monthly basis. But now with LLM integration, I can see myself playing Skyrim basically forever, even well past TES 6 unless a similar mod comes out for that.

      It's my hope that games that prioritize emergent storytelling make use of this technology to extend their lifespans. And if that doesn't happen, I hope that they at least open up their games to modding so that the community can implement it like the cracked Skyrim modding scene.

      submitted by /u/TheSilverSmith47
      [link] [comments]

    5. 🔗 Register Spill Ownership rss

      _The following is an internal Slack message I sent to my Amp teammates after I had a conversation with one of them about ownership. It 's only lightly edited.

      I shared it before, but not here, because I didn't think too much of it. Then today, someone said their CTO shared my post with them and I thought: well, now I have to put it in the newsletter, don't I? So here we are._

      Below, after the Slack post, I added some thoughts on juniors on how I see this advice applying to them.

      Just had a (great) conversation about ownership and engineering here and I realized that I often use the phrase "ownership" or allude to it, but haven't explained what "ownership" means to me in a while.

      So, ownership.

      If I ask you "can you own this?" or "can you take care of this?" or "are you on it?" -- what I'm doing is I'm asking you to own it, to own the solution of a problem from end to end. From "we have a problem" to "we don't have to think about it again."

      That means, when you say that you're owning something, the expectation is that you…

      • Think about what the problem actually is. Maybe you already have a solution in mind, without having thought about what we're actually trying to solve here. Maybe you think "the problem is that we need to migrate from using X to using Y", but that's not a problem, that's a solution. The problem is likely something like "performance is bad", "it's not stable", "it fails for customer x". Maybe there's other possible solutions to that? Think about those. What are the tradeoffs? What's the best solution to go with considering these tradeoffs?

      • Think about edge cases. What are they? Which ones are important? Which ones can we ignore?

      • Think about failures. Network failures are a given, for example. How do we handle them? Retry? Well, how often? How long?

      • Think about data flow. How much data is involved here? Does data need to be migrated? Cleaned up? How can I get my hands on data to properly test this? What invariants are in the data? What assumptions do I have about the shape of the data that I haven't confirmed yet?

      • Think about how you'd test this. How can I know that what I built is correct or not? Are tests enough? Do I need to manually poke at things? Is the difference visible on a screenshot or in a video?

      • How would we announce this? How do we communicate it? Can you picture it? How does it fit into the larger picture of our roadmap? Questions or concerns in that area -- push back! ask!

      • Do the work, with precision, with care, with a sense of urgency, with calmness. Do not half-ass things. Before you merge, ask yourself: am I proud of this? would I show this to John Carmack and say "here's what I built, under these constraints, with these tradeoffs?"

      • Test it manually. Yes, there's automated tests. But in 99% of cases you can manually test or confirm that what you built works: you can run it yourself, you can ask an agent to run through test scenarios, you can poke at the data before and after, you can take screenshots, you can make a demo. Are you sure that what you did actually solves the problem?

      • Make sure it lands in production and works in production. Is it deployed? Did the deploy fail? Do you need to activate a feature flag? Does the feature flag work? Can you use it in production? Can you confirm it's actually deployed?

      • If you think your colleagues needs to know about this change, because it's new feature they should all test, or it's a new convention in codebase, or maybe it's a tricky thing everybody needs to be aware of, or something else: let them know! Do not underestimate peripheral vision: knowing that person X yesterday changed the behavior of how Z works might save person Y three hours of debugging today when a bug report related to Z comes in.

      • Do customers need to know? Who reported the bug? Who's blocked? Let them know.

      • Does the world need to know? Announce that it's out.

      • Are there follow-ups? Do you need to check on what you shipped in the logs? A week later maybe?

      Yes, that's a lot. And there's actually more, because I'm sure I forgot some stuff.

      But that's how you build a product in a small team. We don't have PMs, we don't have a Q&A department. We're small, but we're great , we can do all of that.

      And it's always okay to ask for help, it's okay to ask questions, it's okay to redo things and triple-check. What's not okay is to implicitly assume that someone else will do the things here that you haven't thought about.

      " How does this apply juniors? You can't expect them to really do all of that?"

      I've been asked these questions, or variations of them, after I shared the thoughts above and here's my answer.

      I do not expect juniors to do all of these things right away. But I would expect them to read through the list and aspire to one day be able to do all of these things. Until then, they can and should ask for help.

      In fact, I don't expect anybody to always do all of these things for everything. It's a mental checklist of things to consider -- problem, edge cases, tradeoffs, deployment, customers, messaging, … -- but for quite a few things there aren't edge cases to consider. Or big tradeoffs to weigh. Or deployment is a solved problem. And maybe someone else does the messaging for you.

      And I imagine that most of these things you shouldn't even consider when you work in a company with, say, 5000 employees. I've never worked in a company that large, only startups, so I can't speak to how to successfully ship a software feature at Apple, end to end.

      But when you work in a small company in which there's only a single department, when you want to build things you're proud of, when you work with me and you say own something, I expect you to keep these things in mind and run through them before you declare something as done.

      You know what you should own? A subscription to this newsletter:

    6. 🔗 navidrome/navidrome v0.63.0 release

      Navidrome 0.63 brings a long-awaited upgrade to lyrics: full support for synced sidecar lyrics in multiple formats (TTML, ELRC, SRT, YAML and LRC), including word-by-word karaoke timing and multi-voice (agent) layers, exposed to clients through the new OpenSubsonic v2 lyrics extensions. A huge shoutout to @ranokay, who not only contributed the code for these lyrics enhancements, but also helped shape the OpenSubsonic extension specification behind them. Search also gets noticeably smarter: exact matches now rank above prefix matches (searching for "MØ" brings MØ to the top instead of burying it), and artists with short or non-ASCII names that were previously unfindable now show up correctly.

      The other big theme of this release is performance for large libraries and offline-first clients. Full-library synchronization via search3 (the way clients like Symfonium mirror the whole library) is now flat at every offset instead of degrading with depth, roughly 30-50x faster at deep offsets and about 20x faster for a complete sync on a ~1M-track library, and a related pagination-integrity fix eliminates the duplicate and short pages that could corrupt those syncs. Alongside it, getRandomSongs is about 13x faster on the same library size, and a batch of database improvements (annotation-index- friendly smart playlist filters, leaner list-count queries, and new composite indexes for album/artist song sorts) makes everyday operations anywhere from ~9x to ~160x faster. Finally, a heads-up on a behavior change: sharing is now enabled by default, and can be turned off with EnableSharing=false.

      Security

      • Enforce per-library access on playlist import and sharing paths. In multi-library setups, three read paths did not consistently apply per-library restrictions: M3U import path resolution, shared-playlist track loading, and public share-scoped streams. A user with access to only some libraries could resolve or be served tracks from libraries they weren't assigned to. All three now respect the user's (or share owner's) library access. (#5640 by @deluan)
      • Resolve symlinks to their real target when classifying files during scan, preventing symlinked entries from being misclassified. (ecba19a08 by @deluan)

      Configuration Changes

      Status | Option | Description | Default
      ---|---|---|---
      New | Scanner.ArtistSplitExceptions | List of artist names that must never be split by tag separators (e.g. "Tyler, The Creator"). (#5701) | [] (empty)
      New | Scanner.IgnoreDotFolders | Whether to skip folders whose name starts with a dot when scanning. Set to false to index dot-prefixed folders. (#5568) | true
      Changed | EnableSharing | Sharing is now enabled by default. Set to false to restore the previous behavior. (#5714) | true
      Changed | LyricsPriority | New sidecar lyrics formats added to the default priority list. (#5076) | .ttml,.yaml,.yml,.elrc,.lrc,.srt,.txt,embedded

      For a complete list of all configuration options, see the Configuration Options documentation.

      Lyrics

      • Add structured sidecar lyrics support with OpenSubsonic v2 karaoke cues and agent layers: TTML, ELRC, SRT and YAML sidecar files are now parsed with word-by-word timing and multi-voice information. (#5076 by @ranokay)
      • Allow lyrics plugins to return lyrics in any supported format, not just LRC. (#5632 by @deluan)
      • Keep the player's lyrics in sync on track change and seek, so the previous song's lyrics no longer linger. (7303c9ca4 by @deluan)

      Search

      • Rank exact matches above prefix matches in search results. (#5704 by @deluan)
      • Fix artists with short or non-ASCII names being unfindable after the FTS5 search migration. (#5703 by @deluan)

      UI

      • Add Rosé Pine themes. (#5664 by @draconivis)
      • Fix transient jump to a wrong song when switching tracks in the web player. (#5676 by @deluan)
      • Fix profile self-edits not reporting success or failure. (#5699 by @deluan)
      • Fix Nautiline theme font and width on mobile devices. (#5590 by @devBoi76)
      • Fix DefaultLanguage not being applied on app startup. (#4000 by @deluan)

      Scanner

      • Add Scanner.ArtistSplitExceptions to protect artist names from being split by tag separators. (#5701 by @deluan)
      • Add Scanner.IgnoreDotFolders to allow indexing dot-prefixed folders. (#5568 by @deluan)
      • Fix playlists not being imported when the first scan runs before any admin user exists. (#5609 by @deluan)

      Subsonic API

      • Add OpenSubsonic work and movement attributes, improving classical music metadata for compatible clients. (#5659 by @deluan)
      • Speed up getRandomSongs on large libraries with two-phase random selection, about 13x faster on a 1M-track library. (#5618 by @deluan)
      • Speed up artist search3 pagination at deep offsets, roughly 5-8x faster on a 300K-artist library. (#5620 by @deluan)
      • Speed up search3 empty-query (browse-all) pagination: response times are now flat at any offset, 30-50x faster at deep offsets, making a full ~1M-track library sync about 20x faster overall. (#5601 by @deluan)
      • Make "recently added" album order reproducible and consistent with the RecentlyAddedByModTime setting. (#5678 by @deluan)

      Smart Playlists

      • Extend isMissing/isPresent operators to BPM, bit depth and many text fields. (#5603 by @deluan)
      • Speed up smart playlists that filter on play count, rating or loved status, up to ~14x faster end-to-end (the underlying query alone is over 300x faster). (#5662 by @deluan)
      • Speed up smart playlists with many negated artist/tag rules, ~160x faster on a real-world case with 120 rules on a 323K-track library. (#5607 by @deluan)
      • Fix isMissing/isPresent operators on ReplayGain fields. (#5585 by @deluan)

      Recommendations

      • Match similar and top songs across all artists credited on a track, improving results for collaborations. (#5668 by @deluan)
      • Match ListenBrainz top songs for collaborations using all credited artist MBIDs. (#5670 by @deluan)
      • Speed up top/similar song matching with batched lookups, up to ~14x faster on large batches. (#5635 by @deluan)
      • Fix song matching to use artist credits, so artist-MBID specificity works and collaborators match correctly. (#5637 by @deluan)

      Plugins

      • Add a navidrome plugin CLI for managing and inspecting plugins. (#5682 by @deluan)
      • Expose the song Matcher as a host service for plugins. (#5643 by @deluan)
      • Share plugin DTOs via a common types package, simplifying plugin development. (#5655 by @deluan)

      Artwork

      • Speed up image resizing and WebP encoding/decoding, up to ~30% faster with far fewer allocations. (#5652 by @deluan)
      • Fix artist folder images being incorrectly served as album art. (#5596 by @deluan)
      • Fix WebP crash on 32-bit ARM; WebP encoding is now disabled by default in Docker images. (#5606 by @deluan)

      Transcoding

      • Preserve source metadata when transcoding downloads. (#5628 by @deluan)
      • Enforce server-side player MaxBitRate on all stream paths. (#5611 by @deluan)
      • Honor the player's forced transcoding format in the web UI playback flow. (#5613 by @deluan)
      • Fix partially-written transcodes being served from the cache after a server crash. (#5657 by @deluan)

      Server

      Database

      • Speed up song sorting by album and artist with new sort-order indexes, ~19x faster on large libraries on a cold cache. (#5706 by @deluan)
      • Skip library filtering when a non-admin user has access to all libraries, making song counts ~19x faster on a 920K-track database. (#5696 by @deluan)
      • Skip unnecessary annotation joins when counting items, up to ~9x faster list counts, with even larger gains on a cold cache. (#5694 by @deluan)
      • Make PRAGMA optimize errors non-fatal at startup. (bd3192be0 by @deluan)

      Translations

      New Contributors

      Full Changelog : v0.62.0...v0.63.0

      Helping out

      This release is only possible thanks to the support of some awesome people!

      Want to be one of them?
      You can sponsor, pay me a Ko- fi, or contribute with code.

      Where to go next?

    7. 🔗 HexRaysSA/plugin-repository commits sync repo: +2 releases rss 
      sync repo: +2 releases

## New releases
- [DriverBuddyReloaded](https://github.com/voidsec/driverbuddyreloaded): 2.5.1
- [ida-cyberchef](https://github.com/hexrayssa/ida-cyberchef): 0.3.2
    8. 🔗 r/LocalLLaMA Can you trust local models to answer accurately? rss

      Can you trust local models to answer accurately? | My goal is to improve as a developer, thus I needed to know if local llms can answer technical questions accurately The conclusion is that without rag they don't do too well, but with rag they are very good. Thinking didn't really help, and took so long I only got the scores for e2b and e4b, the rest are still running, it was like only +1% point for thinking. This is what I did:
      - Downloaded the markdown docs from the github repos for the listed projects (Node, Langchain.js, typescript, transformers.js and vue)
      - Used deepseek-v4-flash to generate multiple choice questions based on each markdown file.
      - Benchmarked the unsloth gemma QAT models with thinking disabled on all of these questions
      - Benchmarked the unsloth gemma QAT models with thinking disabled on all of these questions with the correct document added (oracle column)
      - Built a RAG system and benchmarked all the models with thinking disabled, the rag system was not limited to the correct document set as I didn't want to need to select the relevant docset whenever I ask my local llm a question. Was pretty happy that the RAG system worked, it took a fair bit of effort tweaking it to work. So TLDR - local llms, pretty awesome when hooked up to a knowledge base and RAG injects relevant documents before it answers questions. This is a follow on post from my original experiments - now I've included apple intelligence and qwen models as well. Note on apple intelligence, it only has a context length of around 4k, whereas the other models I gave them a context length of 32k. Many of the orcale documents where more than 4k tokens and the rag context injection for the top 5 results also exceeded 4k, so apple intelligence was ran with only top 3 results. So a score of 86% for apple intelligence is pretty strong for a tiny llm included on your device. Edit: Note: Apple Intelligence being tested is AFM 2 3b on device. Thanks to u/mcqwerty197 for pointing that out Edit: These numbers are based on 7,648 multiple-choice questions Edit: For those asking what this is for / what the app is. It is the app I'm making to help me learn first version for iphone is in the app store now https://apps.apple.com/us/app/chatwise-chat-learn/id6784626027 and the update is in review by apple as is the mac version I'll do a post about it, when both the mac and the latest version of iphone one has been through review explain it submitted by /u/Spiritual-Market-741
      [link] [comments]
      ---|---

    9. 🔗 r/LocalLLaMA China’s MiniMax Plans to Launch 2.7-Trillion Parameter Model rss

      https://www.theinformation.com/briefings/exclusive-chinas-minimax-plans- launch-2-7-trillion-parameter-model

      According to The Information, MiniMax plans to launch a new-generation large language model with 2.7 trillion parameters.

      Sources revealed that the internal codename for this new model is M3 Pro. It is expected to be released and open-sourced as early as the third quarter of this year, with significant improvements in handling complex reasoning and multi-step tasks.

      This new model is much larger than MiniMax's current flagship model, M3 (428 billion parameters). Larger-scale artificial intelligence models are more capable of handling complex reasoning and multi-step instruction-based tasks.

      submitted by /u/External_Mood4719
      [link] [comments]

    10. 🔗 MetaBrainz libdiscid 0.7.0 rss

      Version 0.7.0 of libdiscid has been released. libdiscid is a C library that allows applications to easily calculate MusicBrainz and freedb disc IDs from audio CDs or CD TOC details. It also can extract MCN and ISRC information.

      This release fixes several issues with the build system and addresses compiler warnings. As a compile time option it is now possible to have methods returning URLs to use HTTPs instead of HTTP, see then changelog for details. Thanks to Riku Viitanen for this contribution. Also the binary packages now include a build for the Windows ARM64 platform.

      Version 0.7.0 of libdiscid provides the following changes:

      • Add DISCID_USE_HTTPS build flag: If set, the functions discid_get_submission_url and discid_get_webservice_url will generate URLs using the HTTPS protocol instead of HTTP. This might break existing applications that rely on the exact URL structure being returned, hence this flag is disabled by default.
        NOTE: DISCID_USE_HTTPS will become the default in a future release. Please
        update your software to not rely on the exact URL structure being returned.

      • CMake: Fix pkg-config .pc file not being relocatable

      • Autotools: Fix "make docs" not including examples
      • Mac: Fix compiler warning about deprecated use of IOMasterPort
      • Fix compiler warnings about use of strncpy
      • Consistently use CRLF for newlines in versioninfo.rc
      • Provide Windows ARM64 binary builds

      More details on libdiscid, information on available language bindings, and downloads of the source code and pre-compiled binaries for macOS and Windows can be found on the libdiscid page. See also the API documentation for details on how to use the library.

    11. 🔗 anthropics/claude-code v2.1.204 release

      What's changed

      • Fixed hook events not streaming during SessionStart hooks in headless sessions, which could cause remote workers to be idle-reaped mid-hook
    12. 🔗 Ampcode News Agents, Anywhere rss

      You can now start new agents remotely from ampcode.com anywhere you can run amp:

      That means, in addition to running agents in orbs, you can now run agents on any machine you want: your laptop, your server, your cloud dev box, your Raspberry Pi. Your lawn mower even, if it has a shell.

      Enable it by using the command amp: enable remote creation of threads or with the setting:

      // ~/.config/amp/settings.json

{
    "amp.remoteThreadCreation.enabled": true
}

      Once enabled, every Amp client you start will accept and run new threads in its working directory.

      Runner Mode

      You can also use the new runner mode with:

      amp --no-tui

      That starts Amp in a headless mode in which it only waits to start and run new threads:

      You can start multiple runners on the same machine, as long as they're started in different directories. Each runner is uniquely identified by host and working directory. Directories don't have to be version controlled. They can be anything, even home directories.

      You can start agents anywhere now.

      Walkthrough

      Here's Thorsten with a walkthrough:

  4. July 07, 2026
    1. 🔗 IDA Plugin Updates IDA Plugin Updates on 2026-07-07 rss

      IDA Plugin Updates on 2026-07-07

      New Releases:

      Activity:

      • claude-of-alexandria
        • b3b593a6: chore(deps-dev): bump the minor-and-patch group (#88)
        • eb2ad5ee: chore(deps-dev): bump @anthropic-ai/claude-agent-sdk (#87)
        • a400dc61: chore(deps): bump dorny/paths-filter from 4.0.1 to 4.0.2 (#86)
      • ghidra
        • 10b876bb: Merge remote-tracking branch
        • cf3a0900: GP-7047: pull request
        • 6b3ab3ce: Merge remote-tracking branch 'origin/GP-7014-dragonmacher-fixed-toggl…
        • 4b38db9c: Merge remote-tracking branch 'origin/patch'
        • 395cf82b: Merge branch 'GP-7045_ryanmkurtz_swift' into patch
        • c03a70dd: GP-7045: The Swift Demangler now requires Swift to be on the PATH
      • hrtng
        • 6202adf4: Support new and legacy IDA SDK library layouts regardless of version …
      • ida-hcli
        • 05a6b0ba: 0.18.5
        • cf17f573: Improve configuration UX (#244)
        • 167f8e21: python: better detect IDAPython environment with VIRTUAL_ENV set (#245)
      • pyrrha
        • 8b7e05e0: fix(pyproject): stop shipping the mike git dependency in package meta…
        • 8cdf6ac4: bump version
        • 73cd79e4: remove docker package building
        • 420be82d: release: bump copyright years to 2023-2026 for v2.0.0
        • 04b31cc0: bump version
        • f5ebc351: mappers: fix small issues in db handling of decomp mapper
        • c75fb51c: backend: fix func type given by ida
        • 04b7b21c: doc: last small updates before v2 release
        • 5347dbb5: doc: migration to zensical
        • e1216c88: update gitignore with doc artifacts
        • aeba43b6: main: change cli rendering
        • 6b6f55b0: ci: change github ci python version range
        • 34721297: ci: run the backend-free unit tests and merge their coverage
        • 7a1ec69e: test: add backend-free unit tests for inter-image call-graph mappers
        • 598f0502: test: add backend-free unit tests for CLI, decomp and imports mappers
        • 0d6be4da: fix(imports): honor INTERACTIVE duplicate-resolution cache
        • ebcdc434: ci: update coverage targets
        • c0cf6630: fix(ci): preserve per-job coverage data for the global report
        • f2d7bea2: ci: aggregate coverage from all jobs into a global report
        • 003d9622: [fix] ci: reorder stuffs
      • rikugan
        • 89746a3d: Merge remote-tracking branch 'EliteClassRoom/master'
        • e3ba6440: Fixing the output limit to become larger
        • a9405070: fix(ui): ruff format merge + fix _blend_hex typo (upstream undefined …
        • a6c62104: Merge remote-tracking branch 'origin/master'
        • dbe61405: fix(agent): correct relative import depth in _handle_delegate_externa…
        • 8d60120d: fix(ui): bypass IDA 9.4 PyQt5 shim bitwise warning via .value OR
        • c3433f29: fix(ida-ui): try FormToPySideWidget, fall back to FormToPyQtWidget fo…
        • d2a82d0d: fix(ida-ui): shim QtGui.QWidget for IDA 9.4 broken TWidgetToPySideWidget
        • 51a98468: docs(changelog): note idaVersions range fallback for Plugin Manager
    2. 🔗 r/LocalLLaMA Unsloth has uploaded several sizes of Deepseek-V4-Flash GGUF's rss

      Unsloth has uploaded several sizes of Deepseek-V4-Flash GGUF's | submitted by /u/ForsookComparison
      [link] [comments]
      ---|---

    3. 🔗 anthropics/claude-code v2.1.203 release

      What's changed

      • Added a warning when your login is about to expire, so you can re-authenticate before background sessions are interrupted
      • Added a grey ⏸ badge to the footer when in manual permission mode, making the active mode always visible
      • Added the session's additional working directories to MCP roots/list, with notifications/roots/list_changed sent when the set changes
      • Fixed opening or switching background agent sessions on macOS stalling for 15–20 seconds due to a false low-memory detection (regression in 2.1.196)
      • Fixed background sessions becoming permanently unresponsive to attach, replies, and stop when the daemon's session token went stale — the session now recovers automatically
      • Fixed returning to claude agents silently stopping running subagents and re-running the prompt from scratch — their work now carries over
      • Fixed a memory and per-turn CPU regression in interactive sessions: the context-usage indicator no longer re-analyzes the entire transcript after every turn
      • Fixed background agents inheriting a stale PATH from the daemon instead of the dispatching shell, causing missing tools on Windows
      • Fixed background and agent-view sessions dropping a shell-exported ANTHROPIC_BASE_URL, which sent API keys to the default endpoint and failed with 401
      • Fixed Bash failing with "argument list too long" in repos with many git worktrees
      • Fixed worktree-isolated subagents sometimes running shell commands in the parent checkout instead of their own worktree
      • Fixed worktree creation rejecting nested repositories in multi-repo workspaces, leaving background sessions unable to isolate and edit
      • Fixed background agents crash-looping when their working directory was deleted, replaced by a file, or became an invalid path — they now fail once with a clear error
      • Fixed a background daemon auto-upgrade failure silently killing all running background sessions
      • Fixed TaskStop and TaskOutput failing to find background agents spawned by another agent — errors now list running agents by id and description
      • Fixed the claude agents composer discarding your typed message when a slash command isn't available there
      • Fixed the agent list crashing when opening a stopped session whose conversation was already open in another session
      • Fixed background sessions showing "Needs input" in the agent list after the question was already answered
      • Fixed background agent startup failures showing only "exit_with_message" instead of the actual error
      • Fixed background sessions ignoring effortLevel changes in settings.json when forked through the daemon
      • Fixed attached background sessions ignoring CLAUDE_CODE_DISABLE_MOUSE and CLAUDE_CODE_DISABLE_MOUSE_CLICKS opt-outs
      • Fixed /exit incorrectly warning about running background agents after all named agents had completed
      • Fixed background sessions started from a non-git directory unable to edit files when a WorktreeCreate hook was configured
      • Fixed the @ directory picker in claude agents not showing registered git worktrees
      • Fixed background task output on Windows being permanently replaced by an empty file after /clear
      • Fixed content jumping when scrolling up through long transcript history
      • Fixed the terminal flickering and jumping while typing in bash mode when a shell-history suggestion was shown
      • Fixed literal ^[[I / ^[[O escape codes being printed when reattaching to a background session
      • Fixed LSP-only plugins being incorrectly flagged for disuse when their language servers deliver diagnostics or answer navigation requests
      • Improved responsiveness while long responses stream: live-preview updates no longer re-render the whole screen
      • Improved subagent behavior: agents are now less likely to re-delegate their entire task to another subagent
      • Reduced binary size by ~7 MB and startup memory by ~7 MB by loading a large bundled dependency lazily instead of inlining it
      • Changed left arrow to no longer close the background tasks, diff, and workflow detail views — press Esc instead
      • Changed the empty claude agents view to always show the organized sections (Needs input / Working / Completed) with descriptions
      • Removed the startup "claude command missing or broken" warnings — they now appear in /doctor and /status instead
      • Removed a redundant navigation hint from the claude agents footer
      • [VSCode] Added a Settings toggle for "Enable Remote Control for all sessions"
    4. 🔗 Simon Willison sqlite-utils 4.0, now with database schema migrations rss

      This morning I released sqlite-utils 4.0, the 124th release of that project and the first major version bump since 3.0 in November 2020. In addition to some small but significant breaking changes (described in this upgrade guide), this version introduces three major features: database migrations, nested transactions (via a new db.atomic() method), and support for compound foreign keys.

      Database schema migrations using sqlite-utils

      Schema migrations define a sequence of changes to be made to a SQLite database, plus a mechanism for tracking which migrations have been applied and applying any that are found to be pending.

      Migrations are defined in Python files using the sqlite-utils Python library, which includes a powerful table.transform() method providing enhanced alter table capabilities that are not supported by SQLite's ALTER TABLE statement.

      (table.transform() implements the pattern recommended by the SQLite documentation - create a new temporary table with the new schema, copy across the data, then drop the old table and rename the temporary one in its place.)

      Here's an example migration file which creates a table called creatures, adds an additional column to it in a second step, then changes the types of two of the columns in a third:

      from sqlite_utils import Migrations

migrations = Migrations("creatures")

@migrations()
def create_table(db):
    db["creatures"].create(
        {"id": int, "name": str, "species": str},
        pk="id",
    )

@migrations()
def add_weight(db):
    db["creatures"].add_column("weight", float)

@migrations()
def change_column_types(db):
    db["creatures"].transform(types={"species": int, "weight": str})

      Save that as migrations.py and run it against a fresh database like this:

      uvx sqlite-utils migrate data.db migrations.py

      Then if you check the schema of that database:

      uvx sqlite-utils schema data.db

      You'll see this SQL:

      CREATE TABLE "_sqlite_migrations" (
   "id" INTEGER PRIMARY KEY,
   "migration_set" TEXT,
   "name" TEXT,
   "applied_at" TEXT
);
CREATE UNIQUE INDEX "idx__sqlite_migrations_migration_set_name"
    ON "_sqlite_migrations" ("migration_set", "name");
CREATE TABLE "creatures" (
   "id" INTEGER PRIMARY KEY,
   "name" TEXT,
   "species" INTEGER,
   "weight" TEXT
);

      The _sqlite_migrations table is used to keep track of which migration functions have been run. The creatures table above is the schema after all three migrations have been applied.

      To see a list of migrations, both pending and applied, run this:

      uvx sqlite-utils migrate data.db migrations.py --list

      Output:

      Migrations for: creatures

  Applied:
    create_table - 2026-07-07 17:58:41.360051+00:00
    add_weight - 2026-07-07 17:58:41.360608+00:00
    change_column_types - 2026-07-07 18:01:15.802000+00:00

  Pending:
    (none)

      If you don't specify a migrations file, the sqlite-utils migrate data.db command will scan the current directory and its subdirectories for files called migrations.py and apply any Migrations() instances it finds in them.

      You can also execute migrations from Python code using the migrations.apply(db) method, which is useful for building tools that manage their own database schemas over multiple versions. My own LLM tool has been using a version of this pattern for several years now, as shown in llm/embeddings_migrations.py.

      Prior art

      My favorite implementation of this pattern remains Django's Migrations, developed by Andrew Godwin based on his earlier project South. Fun fact: Andrew, Russ Keith-Magee, and I presented our competing approaches to schema migrations for Django on the Schema Evolution panel at the very first DjangoCon back in 2008! My attempt was called dmigrations, developed with a team at Global Radio in London.

      Django's migrations can be automatically generated from model definitions and include the ability to roll back to a previous version. The sqlite-utils approach is deliberately simpler: unlike Django, sqlite-utils encourages programmatic table creation rather than a model definition ORM, so there isn't anything we can use to automatically generate migrations.

      I decided to skip rollback, since in my experience it's a feature that is rarely used. With a SQLite project, an easy way to achieve rollback is to create a copy of your database file before you apply the migrations!

      Migrating from sqlite-migrate

      The design of sqlite-utils migrations is three years old now - I had originally released it as a separate package called sqlite-migrate, which never quite graduated beyond a beta release.

      I've used that package in enough places now that I'm confident in the design, so I've decided to promote it to a feature of sqlite-utils to make it available by default to all of the other tools in the growing sqlite-utils/Datasette/LLM ecosystem.

      I made one last release of sqlite-migrate, which switches it to depend on sqlite-utils>=4 and replaces the __init__.py file with the following:

      from sqlite_utils import Migrations

__all__ = ["Migrations"]

      Any existing project that depends on sqlite-migrate should continue to work without alterations.

      Everything else in sqlite-utils 4.0

      Here are the release notes for this version, with some inline annotations:

      The 4.0 release includes some minor backwards-incompatible fixes (hence the major version number bump) and introduces three major new features:

      I think of migrations as the signature new feature, hence this blog post.

      sqlite-utils has long had a confused relationship with database transactions, partly because when I started designing the library back in 2018 I didn't yet have a great feel for how those worked in SQLite itself.

      Adding migrations to the core library made me determined to finally crack this nut, since transactions make migration systems a whole lot safer and easier to reason about.

      I ended up building this around a db.atomic() context manager which looks like this:

      with db.atomic():
    db.table("dogs").insert({"id": 1, "name": "Cleo"}, pk="id")
    db.table("dogs").insert({"id": 2, "name": "Pancakes"})

      SQLite supports Savepoints, and as a result db.atomic() can be nested to carry out transactions inside of transactions. It's pretty neat!

      This came about when I asked a coding agent to review all open issues and PRs for things that should be included in a 4.0 release since they would represent breaking changes if I added them later, and it correctly identified that compound foreign keys were exactly that kind of feature.

      I started with a breaking change to the table.foreign_keys introspection method, and then decided to see if Claude Fable 5 could handle the more fiddly job of integrating compound foreign key creation into the library. The API design it helped create felt exactly right to me - consistent with how the rest of the library worked already.

      Other notable changes include:

      • Upserts now use SQLite’s INSERT ... ON CONFLICT ... DO UPDATE SET syntax, detect existing table primary keys automatically and reject records that are missing required primary key values. (#652)

      This was the change that first pushed me to consider a breaking-change 4.0 version bump. I built this to help support sqlite-chronicle, which uses triggers to keep track of rows in a table that have been inserted, updated or deleted.

      • db.query() now executes immediately and rejects statements that do not return rows; use db.execute() for writes and DDL.

      Probably the most disruptive breaking change - I've had to update a few places in my own code to switch from db.query() to db.execute() as a result.

      • CSV and TSV imports now detect column types by default, while inserts into existing tables preserve those tables’ column types. (#679)

      The sqlite-utils insert data.db creatures creatures.csv --detect-types flag was a later addition to allow column types (text, integer, real) to be automatically detected based on the data in a CSV. It should be the default, and releasing a 4.0 means I can make it so.

      • table.extract() and extracts= no longer create lookup table records for all-null values. (#186)

      The oldest issue addressed by this release - the underlying bug was opened (by me) in October 2020.

      See Upgrading from 3.x to 4.0 for details on backwards-incompatible changes.

      The detailed release notes for the features and fixes shipped during the 4.0 pre-release cycle are available in 4.0a0, 4.0a1, 4.0rc1, 4.0rc2, 4.0rc3 and 4.0rc4.

      The upgrade guide was entirely written by Claude Fable 5, Claude Opus 4.8 and GPT-5.5. The same is true of the release notes.

      This is the kind of documentation I've slowly become comfortable outsourcing to the robots. It doesn't need to convince people of anything, or express any opinions - its job is to be as accurate and detailed as possible. I've reviewed the release notes closely and can confirm they are accurate and comprehensive.

      Claude Fable 5 helped a lot

      I released the first alpha of sqlite-utils 4.0 over a year ago. I've been dragging my heels on the stable release because of the amount of work it would take to track down and clean up the many other minor design flaws that a major version number allowed me to take on.

      Assistance from Claude Fable 5 (and to a lesser extent Opus 4.8 and GPT-5.5) gave me just the boost I needed to overcome inertia and make the most of the time I could afford to spend on this library.

      Fable has really good taste in API design, and is relentlessly proactive if you give it a more open goal. My most successful prompt was a review task that I issued against what I thought was the last release candidate:

      review the changes on main since the last tagged 3.x release - I am about to ship them as sqlite-utils 4.0, a stable version that promises no backwards-incompatible fixes for a very long time.

      review the changelog and upgrade guide, and write yourself scratch scripts to try out all of the new features in v4 - save those scripts but don't commit them

      I tried this with GPT-5.5 xhigh in Codex Desktop and Fable 5 in Claude Code.

      GPT-5.5 wrote 5 Python scripts and didn't turn up anything particularly interesting - its final report is here.

      Fable 5 wrote 12 scripts, identified 4 release blockers and 10 additional issues in its report, and built a neat combined repro script, which, when run, output the following:

      === 1. Failed db.execute() write leaves an implicit transaction open ===
  in_transaction after failed write: True
  BUG: table 'other' silently lost when connection closed

=== 2. Leading ';' bypasses the query() first-token scanner ===
  BUG: raised OperationalError: no such savepoint: sqlite_utils_query
  BUG: row persisted despite rollback (count=1)

=== 3. Rejected write PRAGMA via query() still takes effect ===
  BUG: user_version=5 after 'rejected' statement (docs say no effect)

=== 4. Implicit compound FK resolves pk columns in table order, not PK order ===
  BUG: other_columns reported as ('b', 'a'), should be ('a', 'b')
  BUG: transform of valid data raised IntegrityError: FOREIGN KEY constraint failed

=== 5. ForeignKey (now a dataclass) is no longer hashable ===
  BUG: cannot use 'sqlite_utils.db.ForeignKey' as a set element (unhashable type: 'ForeignKey')

=== 6. Mixed ForeignKey objects and tuples in foreign_keys= rejected ===
  BUG: foreign_keys= should be a list of tuples

=== 7. insert --csv into an EXISTING table transforms its column types ===
  BUG: existing zip '01234' is now 1234 (column type: int)

=== 8. insert(pk=, alter=True) regression: InvalidColumns before alter runs ===
  BUG: InvalidColumns: Invalid primary key column ['id'] for table t with columns ['a']

=== 9. migrate --stop-before an already-applied migration applies everything ===
  BUG: m2 was applied despite --stop-before m1 (m1 already applied)

=== 10. ensure_autocommit_on() silently commits an open transaction ===
  BUG: row survived rollback (count=1) - transaction was committed

      I found myself agreeing with almost all of them. Here's the PR with 16 commits where we worked through them in turn.

      There's no doubt in my mind that sqlite-utils 4.0 is a significantly higher-quality release than if I had built it without the assistance of the latest frontier models.

      You are only seeing the long-form articles from my blog. Subscribe to /atom/everything/ to get all of my posts, or take a look at my other subscription options.

    5. 🔗 HexRaysSA/plugin-repository commits sync repo: ~15 changed rss 
      sync repo: ~15 changed

No plugin changes detected
    6. 🔗 r/LocalLLaMA Beijing IS NOT looking at curbing overseas access to China's top AI models (Debunking the Reuters report) rss

      The Lie

      Reuters' headline and main narrative: " Beijing is looking at curbing overseas access to China's top AI models ." It portrayed recent Ministry of Commerce meetings as China preparing broad new restrictions on foreign usage of advanced Chinese AI models (including open-weight ones), treating them like a national asset that needs to be locked down from the world.

      The Truth

      The recent meetings (past month) with Alibaba, ByteDance, Z.ai, etc., were primarily about overseas acquisitions, foreign investment, and tech/talent outflow controls and not blocking foreigners from using Chinese AI models.

      Reuters took real meetings on protecting Chinese AI companies and IP from foreign ownership and spun them into a story about restricting model access/usage for the world. They used this document as a "hint" China will restrict their models outside their country but if you read it yourself It tells you a different story.

      The doc shows China wants open source, but they want " trustworthy and controlled" open source. They are trying to solve a specific dilemma: How do we keep flooding the world with free Chinese AI models to crush US tech monopolies, without accidentally letting US venture capital buy up our startups or letting foreign entities reverse-engineer sensitive data from our model weights?

      Scholar Gu Lingyun explicitly warns against over-regulating open weights in the text:

      "If China imposes strict controls on the cross-border flow of open-source weight... the actual effect may only be self-inflicted. Chinese developers will be forced to make a difficult trade-off between compliance and participation

      I encourage people to read the document yourself. It is long but very important to understanding China's strategy on AI going forward.

      submitted by /u/Stannis_Loyalist
      [link] [comments]

    7. 🔗 @HexRaysSA@infosec.exchange Here's the last IDA 9.4 pre-release teaser... mastodon

      Here's the last IDA 9.4 pre-release teaser...

      We've significantly improved Swift binary analysis. In this blog, we're focusing on two different improvements:
      1️⃣ Proper modelling of the Swift ABI
      2️⃣ Proper typing of Swift runtime functions

      👉 https://hex-rays.com/blog/ida-9.4-improved-analysis-of-compiled-swift- binaries

    8. 🔗 r/LocalLLaMA nvidia/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-BF16 · Hugging Face rss

      nvidia/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-BF16 · Hugging Face | Nemotron-Labs-3-Puzzle-75B-A9B is a deployment-optimized large language model developed by NVIDIA, derived from Nemotron-3-Super-120B-A12B. The model is produced using Iterative Puzzle, a post-training compression framework, with the goal of significantly improving inference efficiency for interactive, reasoning-heavy, and long-context workloads while preserving strong downstream accuracy. The model employs a hybrid MoE architecture with interleaved Mamba, MoE, and Attention layers. Like Nemotron-3-Super, it supports Multi-Token Prediction (MTP) for faster text generation. Compared to its parent, Puzzle-75B-A9B reduces the model from 120.7B total / 12.8B active parameters to 75.3B total / 9.3B active parameters. See the tech report for full training and compression details: Nemotron-Labs-3-Puzzle-75B-A9B: Compressing Hybrid MoE LLMs. Compared to Nemotron-3-Super, Puzzle-75B-A9B:

      • Achieves approximately 2× higher server throughput on a single 8×B200 node at matched user-throughput constraints,
      • Increases sustainable 1M-token single-H100 concurrency from 1 request to 8 requests,
      • Maintains strong accuracy across reasoning, coding, multilingual, long-context, and agentic benchmarks.

      The supported languages include: English, French, German, Italian, Japanese, Spanish, and Chinese. This model is ready for commercial use. submitted by /u/jacek2023
      [link] [comments]
      ---|---

    9. 🔗 r/LocalLLaMA Beijing is looking at curbing overseas access to China's top AI models (Reuters) rss

      Beijing is looking at curbing overseas access to China's top AI models (Reuters) | Reuters: Beijing is looking at curbing overseas access to China's top AI models, sources say: https://www.reuters.com/world/beijing-is-looking-curbing-overseas-access-chinas-top-ai-models-sources-say-2026-07-07/ submitted by /u/Nunki08
      [link] [comments]
      ---|---

    10. 🔗 modem-dev/hunk v0.17.0 release

      What's Changed

      • fix(ui): avoid mixed theme preview frames by @benvinegar in #458
      • feat: add agent skill menu dialog by @benvinegar in #469
      • feat(ui): add menu bar visibility toggle by @eduwass in #476
      • feat: highlight .mts and .cts files as TypeScript by @scaryrawr in #485
      • Recommend installation from the main homebrew tap by @lugray in #498
      • test(pty): isolate integration tests from ambient user config by @narthur in #494
      • test(pty): speed up horizontal-scroll integration tests by @narthur in #495
      • bugfix: Let draft notes own keys in pager mode by @skatkov in #480
      • Update hunk installation instructions in README by @fraluc06 in #497
      • fix(session-broker): keep live sessions alive across machine sleep by @abgregs in #492
      • fix: unblock CI (README formatting, note-geometry scroll clamp, test config isolation) by @benvinegar in #506
      • feat(nix): add enableClaudeIntegration home-manager option by @rtimush in #383
      • fix: quit only on q shortcut by @benvinegar in #505
      • feat(jujutsu): add enableJujutsuIntegration to home-manager options by @debarchito in #482
      • fix(session-broker): detect Windows bunfs paths when auto-launching the daemon by @benvinegar in #507
      • test: clean up isolated config homes after integration runs by @benvinegar in #509
      • fix: preserve transparent TUI diff row tints by @sunanmau5 in #510
      • refactor: remove unused withTransparentBackground theme helper by @benvinegar in #511
      • feat: add support line number in "Open file in editor" for helix by @iniw in #496
      • chore(deps): bump the github-actions group with 3 updates by @dependabot[bot] in #501
      • chore(deps): upgrade OpenTUI to 0.4.2 by @benvinegar in #513

      Full Changelog : v0.16.0...v0.17.0

    11. 🔗 HexRaysSA/plugin-repository commits sync repo: +1 release rss 
      sync repo: +1 release

## New releases
- [ida-cyberchef](https://github.com/hexrayssa/ida-cyberchef): 0.3.1

generated: July 10, 2026 at 06:31:51