Fogbank for Code, Pinned Carriers, and a Frontier Model in 90 Gigabytes

It's a Q2 mixed dynamic quant recipe (Q2 experts and Q4 the rest) thanks to @antirez's tip (90GB on disk).

• DeepSeek-V4-Flash, posted to mlx-community, lands at 90GB on disk in a 2-bit dynamic quant — small enough to run on 128GB Macs.
• The recipe quantizes experts to Q2 and the rest of the model to Q4, a mixed scheme suggested by Salvatore Sanfilippo (antirez).
• Pre-quantized weights for the full V4 collection are now hosted by the MLX community org thanks to compute support from Lambda.
• Yesterday's brief had this as a passing mention; today it has shipped weights and a working laptop demo.

VT-1 is now pinned to CT-1. VT-2 tries to enter the same synchronized block. It blocks waiting for the monitor. VT-2 is now pinned to CT-2. All carrier threads are now pinned. No carrier is available.

• Java 21 virtual threads multiplex M virtual threads onto N carrier threads via a ForkJoinPool; the throughput win comes from cheap unmount/remount around blocking I/O.
• synchronized blocks pin a virtual thread to its carrier because monitor ownership is tracked by OS thread identity, not virtual thread identity — letting a different VT run on the same carrier would let it re-enter a lock it never acquired.
• Pinning is not a bug by itself; it becomes a deadlock when enough carriers are pinned that no virtual thread can make progress, which is the failure Netflix and Atlassian both hit.
• ReentrantLock uses LockSupport.park, which is virtual-thread-aware, so it unmounts cleanly. Java 24 removes the synchronized pinning behavior entirely (JEP 491).
• The runnable demo with -Djdk.virtualThreadScheduler.parallelism=2 reproduces the exhaustion in 6 seconds versus 2 seconds with ReentrantLock.

It's Fogbank for code. When juniors skip debugging and skip the formative mistakes, they don't build the tacit expertise. And when my generation of engineers retires, that knowledge doesn't transfer to the AI.

• Stetskov draws a parallel between the US defense industry losing the ability to build Stinger missiles after a 20-year production gap, and what he sees happening to the junior software engineering pipeline today.
• The Fogbank case — a nuclear-warhead material the US lost the recipe for because the impurity that made it work existed only in retired workers' heads — is the load-bearing analogy.
• He cites the same METR study from yesterday's brief: experienced devs predicted AI would make them 24% faster, came in 19% slower, with a 43-point gap between prediction and reality.
• His own hiring data: 2,253 candidates, 4 hired — a 0.18% conversion rate for the rare combination of technical skill and judgment to override a confident-sounding model.
• Defense ramp-ups took three to ten years to recover from optimization away. Software senior pipelines are on a similar timescale.

Open API spec is 2.3 million tokens. Into tools, that's something like 1.1 million tokens. And that's never going to fly even with the biggest foundational models.

• Cloudflare's full API is 2,600+ endpoints; loaded as MCP tool definitions, the schemas alone consume roughly 1.1 million tokens.
• Splitting one company's API across 16 product-scoped MCP servers gives you context discipline but loses coverage — most servers ship a fraction of the underlying API.
• Three patterns for progressive discovery: agent-driven CLIs, in-protocol tool search, and code mode where the agent writes typed code against a generated SDK and runs it in a sandboxed isolate.
• The objection to code mode was always 'untrusted code is scary' — Cloudflare's answer is V8 isolates with explicit network and filesystem capability flags.
• Carey's framing: MCP isn't dead, but the default of 'load every tool into context' is dead. The protocol can carry CLIs, tool search, and code mode equally well.

The parameter we had been setting this entire time had nothing to do with power sequencing, it was the minimum refresh rate toggle for VRR. Setting it to 0 before a modeset simply tells DCP to disable VRR.

• Three years of work in 6.x kernels gives way to Linux 7.0 with substantial Asahi gains: ALS-driven True Tone-style color, PMP power management saving roughly half a watt at idle on M1 Pro (a 20% drop), and Bluetooth audio coexistence finally landing.
• The VRR discovery is a textbook reverse-engineering moment: a parameter the team had set to 0 for years assuming it was a power-on quirk turned out to be the adaptive-sync minimum refresh rate, encoded in fixed-point as 0x300000 for 48Hz.
• Asahi's installer pipeline now runs through GitHub Actions instead of manual CDN admin steps — the previous installer tag was from June 2024, which had become a real problem for distros downstream.
• The audio stack writeup includes a generic ASoC bus-keeper API now merged for 7.1, addressing how Apple chains amp data lines in series — a quirk that may help other embedded platforms too.

Even if AMD is now beat on battery efficiency, it's worth remembering that the HX370 still performs (exactly!) as well as Panther Lake on heavy multi-core runs like HEY's 30K-assertion test suite. Might be some good deals to be had on those machines soon!

• DHH benchmarks on a real workload: HEY's 30K-assertion test suite, where the AMD HX370 ties Intel's new Panther Lake.
• Panther Lake's claimed advantage is battery efficiency, not raw multi-core throughput — a relevant distinction for developers buying laptops to run tests, not laptops to read email on a plane.
• The implicit point: synthetic benchmarks are not your test suite, and 'last generation' silicon often holds up better than the marketing cycle suggests.

• Pitch is a single API across 500+ LLM and 'expert' models (OCR, STT, TTS, vision, translation), with smart routing, region pinning, and provider fallbacks.
• The hook for an EU buyer is data residency and GDPR posture — OpenRouter routes to whichever inference provider wins the price auction, which is fine for hobby code and not always fine for regulated workloads.
• It's a thin layer; the value is in the routing config, fallback ladder, and audit trail, not the inference itself.

• mlx-community now hosts a full DeepSeek V4 collection of pre-quantized weights, sponsored by Lambda compute.
• Companion tweet notes that traditional Apple Silicon profiler metrics often don't match end-to-end performance — saving 10ms in a module can show no overall improvement, so you need custom metrics.

DeepSeek-V4-Flash powering 4 parallel agents on Pi (by @badlogicgames). Running on M3 Ultra at ~30-34 tok/s and 160-187GB peak URAM using MLX-LM.

• DeepSeek-V4-Flash (284B total / 13B active params) running on M3 Ultra at 30-34 tok/s
• Four parallel agents running simultaneously on a single Mac
• Peak 160-187GB unified RAM using MLX-LM
• Collaborative effort involving 0xClandestine, pcuenq, kernelpool, ivanfioravanti and others

DeepSeek-V4-Flash-2bit-DQ coming to the mlx-community HF! It's a Q2 mixed dynamic quant recipe (Q2 experts and Q4 the rest) thanks to @antirez's tip (90GB on disk).

• 2-bit dynamic quantization of DeepSeek-V4-Flash
• Q2 mixed quantization: Q2 for experts, Q4 for the rest
• Weights compressed to ~90GB on disk
• Enables running on 128GB Macs
• Recipe came from antirez (redis creator)'s suggestion

Pi has implemented the best agent loop that I have read, the pi-mono/agent is only a few files and I use it for teaching the topic. It's the simplest, most efficient harness token wise. Highest cache hit rate, lowest tokens per session, least bugs.

• Pi agent loop described as the best available
• pi-mono/agent is only a few files
• Highest cache hit rate among available harnesses
• Lowest tokens per session, least bugs
• Being used for teaching the topic

I've open sourced my Pi + Ax (dspy in ts) + GEPA experiment here. It's an experiment, and completely vibe coded, so don't expect much. I haven't put it through its paces yet, or tested to see how it compares to pi normally, but it does seem to work pretty well with qwen 3.5 9B.

• Combines Pi harness with Ax (DSPy equivalent in TypeScript) and GEPA
• Model-specific prompt optimization via GEPA
• Automatic tag conversion between tools and XML
• Open sourced as an early experiment
• Working with qwen 3.5 9B

Built a whole app that spins up pi workers in docker per default. Works fast and easy. If you have plugins, you can just mount the folder or copy via Dockerfile. From there you can do a bunch of interesting orchestration. Tell your agent to check the pi docs and build a config.

• Docker-based orchestration of Pi workers
• Plugin mounting via Dockerfile or folder mount
• Agent-driven configuration building from pi docs
• Focus on orchestration rather than model quality

Pi is just incredible. works reliably, renders fast, no complexity, /tree, great sdk, token efficient.

• Reliable operation
• Fast rendering
• Low complexity
• /tree context management support
• Good SDK
• Token efficiency

wonder what you can come up with using a harness that's actually maleable. doesn't have to be pi, obv.

• Malleable harnesses as a design principle
• Pi as the exemplar but not the exclusive option
• Focus on framework flexibility over rigid abstraction

I feel sorry for Claude Code. I know they're not the one. I'm not overcommitting — not investing too hard. I wonder if they know I'm pulling away.

• Matt Pocock signaling disengagement from Claude Code
• Explicitly not overcommitting or investing too hard
• Implies Claude Code is not his primary tool going forward
• 555 likes, 96 replies, 118K views

When my generation of engineers retires, that knowledge doesn't transfer to the AI. It just disappears.

• Fogbank story: nuclear weapons material lost when production expertise retired, $69M to reverse-engineer
• Defense industry consolidation: 51 major contractors collapsed into five, workforce fell 65%
• France halted domestic TNT production in 2007; restarted only in 2024
• Stinger missile production took 30 months minimum from order to delivery
• Junior developer timeline: 3-5 years to mid-level, 5-8 to senior, 10+ to principal — can't be compressed by AI
• METR study: experienced developers took 19% longer on real-world tasks with AI tools, predicted 24% speedup
• Salesforce won't hire more engineers in 2025; 54% of engineering leaders expect AI to reduce junior hiring long-term

• Stanford researchers fed a language model DNA sequences and asked it to create a new virus
• Model wrote hundreds of viral sequences, 16 of which worked
• One of the 16 functional sequences used a protein that doesn't exist in any known organism on Earth
• Study published on bioRxiv: https://www.biorxiv.org/content/10.1101/2025.09.12.675911v1.full.pdf

• Gas power projects for just 11 US data center campuses could emit more greenhouse gases than entire countries
• Report from PC Gamer citing energy data

• European alternative to OpenRouter
• Model routing API covering multiple providers
• Front page on HN with 88 points and 43 comments

• Asahi Linux reached kernel 7.0 after almost three years of 6.x series work
• VRR (Variable Refresh Rate) support for external displays
• PMP (Power Management Processor) driver saves ~0.5W idle, 20% decrease in idle power
• Bluetooth WiFi coexistence fixes prevent audio dropouts
• Installer now deploys automatically via GitHub workflows
• Bus keeper API merged for upstream speaker amp support
• Reverse engineering of undocumented Apple firmware interfaces continues

• Posted a useful image for thinking about AI capability curves
• Image designed to help people think about trajectories intuitively
• 976 likes, 18 replies, 112 retweets