Show HN: I shrank DeepSeek V4 Flash to 57GB and it wrote a compiler on my Mac https://ift.tt/gqJoVpN

Show HN: I shrank DeepSeek V4 Flash to 57GB and it wrote a compiler on my Mac I built a specialized package of DeepSeek V4 Flash 0731 (originally 284B total parameters, 13B active), preserving reasoning, tool calling and coding capabilities: https://ift.tt/8A9K2wH... I let it write a minimal C compiler targeting ARM64, then test the result with Fibonacci and FizzBuzz programs, and it succeeded in less than 1 hour, with the full recording at: https://youtu.be/XiwSilmV8B0 You can run it on Silicon Macs with my engine https://ift.tt/5msD9i6 , while one of the core libraries developed to obtain this result is available at https://ift.tt/WfuENg1 . The above recording was on a 128GB memory MacBook M3 Max, but you can also run it on 32GB MacBooks with a very usable context (128K tokens) and projected 5 tok/s. I did try it on a fanless 16GB memory MacBook Air M1 (1.39 tok/s), but unfortunately the available context was very small. How: - First, efficient quantisation: mlx-iqk takes advantage of IQ_K tensor encoding, more efficient than the ones available via llama.cpp or barebones MLX, originally designed by Iwan Kawrakow - I also changed the layout to a k-contiguous one, to make it faster, at least in this Metal setup. - Second, expert pruning: each of the 40 learned-router layers had 256 experts, and not all of them are equally important for the coding use cases. I removed 80B parameters - this is a known technique called REAP, shared at https://ift.tt/h8obxUX . - Third: balancing the cheapest IQ1_S_R4 tensor encoding (~1.5 bits per weight), selectively promoting projections to IQ2_KS or IQ2_K where the measured error reduction justified the bytes. One of the main ideas was not only to save relevant knowledge, but also to not make it forget how to... stop thinking, how to use reasoning. In the first experiments, it would sometimes reason for thousands of tokens without closing its thinking section, or it would go in loops. Then I solved this by heavily weighting tool-calling traces and structured reasoning in the calibration mix. https://ift.tt/WlGR70t August 17, 2026 at 12:13AM

Show HN: Sib - Unixy LLM Client using Git to store converastions, instead SQLite https://ift.tt/8yrOFpA

Show HN: Sib - Unixy LLM Client using Git to store converastions, instead SQLite Hi all :) The reason I like git is that the source tree snapshot and the commit structure itself are always immutable, and destructive operations are just renaming a ref file. Once you understand that, no matter how hard the CLI is to make sense of, you never hesitate to run a command. You can always get it back. LLM conversations aren't as fragile to change as a source tree, but for me an auto-generated SHA-1 hash is more comforting than an auto-generated session title ^~^ The project is in its early stages and contributions are welcome. If you've worked with git plumbing commands, it'll be easy to hack on - and I think it'll be pretty fun. Original: https://ift.tt/kj108vu... https://ift.tt/1WOA3gF August 16, 2026 at 10:35PM

Show HN: ElevenMusic Sounds, loop library that grows as other people generate https://ift.tt/hpmQcjV

Show HN: ElevenMusic Sounds, loop library that grows as other people generate https://ift.tt/wl06tSG August 16, 2026 at 05:22AM

Show HN: Live Claude Usage HUD for a $38 Thermalright Trofeo Vision LCD https://ift.tt/jhypkr7

Show HN: Live Claude Usage HUD for a $38 Thermalright Trofeo Vision LCD https://ift.tt/FvkMKsd August 16, 2026 at 04:42AM

Show HN: Every MUNI, BART and Caltrain in SF on a Live Map https://ift.tt/mf08HNa

Show HN: Every MUNI, BART and Caltrain in SF on a Live Map https://ift.tt/wa9GdI7 August 16, 2026 at 02:09AM

Show HN: Self-hosted monitoring for AI recommendations (MIT License) https://ift.tt/c8IgVqC

Show HN: Self-hosted monitoring for AI recommendations (MIT License) https://lettertrace.com August 16, 2026 at 12:35AM

Show HN: Is AI Dumber Today? An index of AI model experience from user's opinion https://ift.tt/DGI7zUA

Show HN: Is AI Dumber Today? An index of AI model experience from user's opinion https://isaidumber.today/ August 14, 2026 at 08:50PM

Show HN: OJCP – an open protocol for agent-consumable job data https://ift.tt/7Zt0wQh

Show HN: OJCP – an open protocol for agent-consumable job data Author here! Agents are applying to jobs for people right now, with progressively more volume, and there's nothing built for it. So they scrape career pages and fight ATS forms with Playwright/Browser Use, which breaks constantly (or they get bot blocked). Employers get buried in applications that don't fit, candidates hear nothing back, and the resume is now an AI-written thing that another AI scores (which breaks the existing model entirely, btw). OJCP is MCP tools for search and apply, a manifest at /.well-known/ojcp.json so agents can find providers, and schemas that extend schema.org instead of replacing it. The playground on the site is a live MCP endpoint, so you can throw calls at it right now. Why a spec at all when models keep getting better at figuring things out? Inference can't produce authorization. An agent can work out what a form wants. It can't establish that someone consented to this specific submission, and then employer has no way to verify who's calling. So TL;DR a more capable agent is also a more capable impersonator. In this model, trust runs both direction. Agents sign requests using the same method that CloudFlare and OpenAI are already using, providers sign their manifests, agents can check against a JWKS, and trust tiers cap how much candidate PII can go to a given provider. Validation happens at consent, so browsing costs nothing and you only pay the verify when the interaction occurs. I'm the CTO of Recruitics (job advertising) and spent time at LinkedIn before that, so I've been at the intersection of hiring and job search for a while and have felt the pain of both sides. Happy to answer any questions! https://ojcp.dev/ August 12, 2026 at 10:27PM

Show HN: At 16K features, flat autoencoders break. Curved space doesn't https://ift.tt/xZKYFCE

Show HN: At 16K features, flat autoencoders break. Curved space doesn't https://ift.tt/BDAjqdJ August 13, 2026 at 11:16PM

Show HN: Dynobox – A test runner for AI agent skills and workflows https://ift.tt/SnDFqQc

Show HN: Dynobox – A test runner for AI agent skills and workflows https://ift.tt/YS5V9RM August 12, 2026 at 11:14PM

Show HN: Pulp – Zero-allocation C11 telemetry engine (28M logs/SEC to disk) https://ift.tt/jvGIe2c

Show HN: Pulp – Zero-allocation C11 telemetry engine (28M logs/SEC to disk) https://ift.tt/35Sne8Y August 12, 2026 at 01:45AM

Show HN: HyperSAE – Hyperbolic Sparse Autoencoders for LLM Interpretability https://ift.tt/61D3Ccg

Show HN: HyperSAE – Hyperbolic Sparse Autoencoders for LLM Interpretability https://ift.tt/0MjiICg August 12, 2026 at 01:19AM

Show HN: Proxima serves 4x more requests with no hardware change on vLLM https://ift.tt/baq9wOc

Show HN: Proxima serves 4x more requests with no hardware change on vLLM hey everyone, i decided to make a vLLM plugin that implements the Star-KV paper. the results are quiet promising with a decode kernel thats faster than FA2 in higher batch sizes. would love any thoughts and recommendations https://ift.tt/5MAN4cC August 11, 2026 at 10:04PM