How I Fight AI Brain Fry
Article URL: https://sergeykuhne.com/blog/fighting-ai-brain-fry Comments URL: https://news.ycombinator.com/item?id=50416363 Points: 83 # Comments: 56
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Article URL: https://sergeykuhne.com/blog/fighting-ai-brain-fry Comments URL: https://news.ycombinator.com/item?id=50416363 Points: 83 # Comments: 56
Hello HN!I've always had a bad time redistributing programs that use shared libraries, AppImage in my opinion does too much, and a container is an overkill - not always integrates smoothly.I've moved to nix since long ago, but redistributing between nix and other distros or vice versa it's still a pain in the ass, more so if you want to run an alien app in nix or a nix app without the package manger in any other distro.For that, with the help of Claude, I've implemented a format that deduces at pack-time all the shared libraries a program needs, puts them, together with its own glibc and a patched dynamic linker into a single executable.This means that you don't actually need a runtime nor any special capability, it can run inside a FROM SCRATCH docker, alpine linux etc... And it also supports coreutils-like multiple programs accessible via symlinks, so it is a good fit for that purpose.You can just use:> felf pack $(which git) --output git.felfand you'll have already a git binary that works everywhere, no matter which distro you are building this from. This only packs the binary and their dependencies.Essentially, the effect is similar as if you LD_PRELOAD's all the libraries that it requires.In the end, we have been fighting these demons since long long ago, chroots/docker/appimage/flatpack/nix etc...So this xkcd applies: https://xkcd.com/927/I'm happy to share the project, I hope it inspires new discoveries/tools in this regard! Comments URL: https://news.ycombinator.com/item?id=50415219 Points: 2 # Comments: 0
Article URL: https://github.com/dooly6276/claude-bulkhead Comments URL: https://news.ycombinator.com/item?id=50419582 Points: 2 # Comments: 0
Article URL: https://github.com/Shann5/tab-league Comments URL: https://news.ycombinator.com/item?id=50416248 Points: 1 # Comments: 0
We check out the ASRock Rack DMRD16-2L+ which is a 16-channel Intel Diamond Rapids Xeon motherboard with 128x PCIe Gen6 lanes over MCIO The post ASRock Rack DMRD16-2L+ Spotted with 16 DDR5 Slots for Diamond Rapids appeared first on ServeTheHome.
The latest update to llama.cpp introduces enhanced MoE (Mixture of Experts) cache functionality, enabling computation of cache misses on the CPU with a configurable CPU share. A new tuning script, moe-cache-tune.py, automatically determines optimal cache size and CPU share settings based on system performance. The update also improves scheduling efficiency, removes redundant sync operations, and refactors code for better maintainability, while maintaining compatibility across multiple platforms including macOS, Linux, Windows, and Android.
The llama.cpp release b12195 introduces a change to refuse device buffers that would exceed VRAM, improving memory management. It includes precompiled binaries for multiple platforms including macOS, Linux, Windows, Android, and openEuler, with support for various backends such as CUDA, ROCm, Vulkan, OpenVINO, SYCL, and others. Some builds are marked as disabled or experimental.
The ggml library has been updated to make tied selections in the radix Top-K algorithm deterministic on CUDA and Metal backends by replacing atomic compaction loops with a deterministic scheme using simdgroup scans and coalesced memory accesses. This change ensures byte-identical outputs across runs, improves performance by 20-45% on M5 Ultra hardware, and enables deterministic testing for Metal and ROCm. Redundant CUDA kernel resets were removed, and Vulkan/Metal changes were reduced or skipped.
Now, with this open-source tool everyone can learn,build,share,and validate the system design.This is next step, drawing design on blank page to making it a workflow .Do explore and let me know how it is... Comments URL: https://news.ycombinator.com/item?id=50412135 Points: 1 # Comments: 0
I built Coldframe, a honeypot that pretends to be a supermarket refrigeration controller. Inspired by the events on industrial fridge installations in the past weeks, I wanted to see what people would try against a convincing fake.To learn more about this I built a honeypot that resembled perfectly the Danfoss AK-SM 800A and Dixell XWEB interfaces. Both of them are recognized on Shodan as real ones (an not as honeypots). It’s one Python file using only the standard library, and it builds on eppser/danfoss-ak-sm-decoy and public research by Synacktiv, Claroty and Ironhackers.Things that were fun to get right:- Matching real units. I used Shodan records of real devices to match header order, page titles, certificate fields and TLS versions. The Danfoss persona sends nginx headers in the same order as a real AK-SM 800A. The Dixell persona sends no Server header and pads its page to exactly 8,202 bytes.- Recognising exploit attempts. It recognises CVE-2025-41450, the Danfoss “code of the day” login bypass, and records whether the submitted code is correct for today. It also tags attempts targeting three other Danfoss CVEs and the XWEB vulnerabilities.- Letting them in. Default logins open a fake store dashboard with defrost buttons, so you see the second move, not just the first. Nothing behind it is real.Two instances have been live for a day. So far they’ve only seen generic scanners and Shodan’s crawler, which is called refrigerator.census.shodan.io.Feedback welcome, especially from anyone who has seen these devices in the field! Comments URL: https://news.ycombinator.com/item?id=50406292 Points: 15 # Comments: 1
Nino is a source-generated binary serializer for C#, and as far as we know the fastest one. In the same BenchmarkDotNet run on GitHub runners against MemoryPack 1.21.4, it serializes classes and polymorphic types 3.5–7.2x faster and deserializes them 1.7–2.1x faster (arrays of unmanaged structs are on par). Deserializing into existing objects allocates nothing. Reproduce: https://nino.xgamedev.net/en/docs/v4/benchmarksIt is built to read bytes you do not trust: lengths are checked before allocating, depth is bounded, hash flooding is limited, and the read paths are fuzzed in CI.The bytes are a spec, not a memory dump, so CoreCLR, Mono, IL2CPP and 32-bit hosts write the same bytes, and runtimes for TypeScript, Go and Rust pass the same conformance corpus.Trade-offs: positional by default (VersionTolerant or Keyed for stored data), a 4-byte type id on polymorphic values, no System.Type, needs C# 9 and Roslyn 4.3+.Docs: https://nino.xgamedev.net Comments URL: https://news.ycombinator.com/item?id=50413601 Points: 1 # Comments: 0
My recipe app picked up users when a similar app shut down, and I kept noticing people scrambling to export their data, so I created this project to scratch my own itch.The 3 main purposes this serves are:1. Give the public a way to find how to export their data or find a replacement if one of the apps they use shuts down.2. Give app creators a way to get their apps in front of people who are actively looking for a replacement for a similar app. Sponsored spots are labeled and never change the dates or export steps.3. Provide inspiration for app creators looking for ideas for what to build next. Just because apps shut down doesn't mean they were not good ideas; many times they shut down for other reasons, like poor execution, no marketing, over-spending, etc.Every date and export step links to the company's own notice.I'm always open to ideas to make it more useful. I read all the comments and constructive criticism and input are always welcomed. Comments URL: https://news.ycombinator.com/item?id=50412639 Points: 1 # Comments: 0
I've been beavering away at this since July, and its finally getting to a point where its stabilising.It started Windows only, but I recently converted over to Linux, so I've been ironing out the kinks in the last week. You can install it via apt or rpm, or .exe or .msi on Windows.Many more features to come, including native agent support and more preview features (images, PDF, document formats). Claude works pretty well in it, but I've not tried many other agents yet.I rarely leave throng now - 99% of my work happens inside it. I stopped using Visual Studio around 10 months ago (in favour of VSCode), and now I don't even use VSCode. Comments URL: https://news.ycombinator.com/item?id=50412226 Points: 1 # Comments: 0
Article URL: https://github.com/volcengine/OpenViking Comments URL: https://news.ycombinator.com/item?id=50410477 Points: 1 # Comments: 1
The apps and platforms that help family caregivers manage their loved ones’ needs are becoming more capable, but the broader issue of caregivers’ well-being remains unsolved.
The release adds the Kimi-K3.1-Pro text model to llama.cpp, replacing full-attention MLA layers in K3 with DSA (Dynamic Sparse Attention) layers featuring a lightning indexer. The indexer uses an additive mask over the cache and reuses sparse flash attention paths, while preserving existing KDA layers and MoE components. The model's architecture includes four indexer tensors per DSA layer, hybrid memory for caching, and maintains quantization integrity by excluding indexer parameters from quant overrides. Verified against HF checkpoint with identical PPL beyond context length, and supports multiple platforms including macOS, Linux, Windows, Android, and iOS.
A fix for shared memory bank conflicts in the lightning indexer has been implemented for CUDA support in llama.cpp version b12185, improving performance and stability on CUDA-enabled devices.
Run Claude Code as a remote agent on Amazon Bedrock AgentCore. Each coding task runs in its own isolated session in your AWS account, every model call goes through Amazon Bedrock, and the result comes back as a GitHub pull request you review and merge.
Greenlight Financial resolves more than 90% of up to 4,000 monthly security and risk cases without human intervention using Luna, a tiered multi-agent system on Amazon Bedrock AgentCore. This post covers the architecture, episodic memory for continuous learning, and the expansion from SOC operations into financial risk triage.
Amazon SageMaker Inference now supports GPU partitioning with NVIDIA Multi-Instance GPU (MIG), which divides one GPU into hardware-isolated partitions so you can run a different model in each or take a right-sized fraction of a newer accelerator. This post shows how two optional API fields place one model per partition, and what we measured against a whole older GPU.