Show HN: Claude Code status line, hooks and permissions builders (in-browser)
Article URL: https://orbtile.com/tools/claude-code-statusline-builder Comments URL: https://news.ycombinator.com/item?id=50415399 Points: 1 # Comments: 0
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Show HN:Free Web Tools
Article URL: https://nyneon.net/tools/ Comments URL: https://news.ycombinator.com/item?id=50418253 Points: 3 # Comments: 2
Show HN: Hyper Compress: The Best Open Source PDF Compression Engine
Article URL: https://github.com/alam00000/bentopdf-hyper-compress Comments URL: https://news.ycombinator.com/item?id=50416782 Points: 1 # Comments: 0
Show HN: Record, branch, and compare command runs in stdlib-only Python
Hi, this is a package I developed for keeping track of short toy-model development cycles and explaining what changed between runs.The core idea: commands run through the package, and their declared parameters (or injected parameters with --substitute) and outcomes go into a dependency-free ledger stored in the project. Because that ledger lives in the project directory, it has worked well for passing context between different agents. I’ve also found some use for it in CI work. Comments URL: https://news.ycombinator.com/item?id=50417782 Points: 3 # Comments: 0
b12202
The llama.cpp v1.220.2 release introduces enhanced BF16 precision support across multiple components, aligning with reference implementations by moving specific operations like GLM4 family projections, MiMoVL vision matmuls, and Qwen3-TTS speaker encoder conv1d to BF16 where appropriate. Backends such as CUDA, Hexagon, OpenCL, WebGPU, and Vulkan now uniformly handle GGML_PREC_BF16 similarly to F32, while CPU, SpaceMIT, and ET backends extend flash attention support to BF16 with F32 accumulation. Flash attention tests now include BF16 cases, and the KQ multiplication in Llama is updated to request BF16 precision. A TODO remains in clip.cpp for future work.
Send contracts for e-signature from n8n, with TurboDocx, Slack approval and SharePoint filing
This Verified Node Spotlight is a guest post written by Nicolas Fry, CEO of TurboDocx.The deal is closed and the contract is ready. It's been sitting in someone's inbox for three days, waiting for an "OK, send it." Once it's finally
Show HN: AIDA – agentic generated user guides
Article URL: https://github.com/tryaida/aida Comments URL: https://news.ycombinator.com/item?id=50412923 Points: 2 # Comments: 1
Show HN: Ferrule – copy-in React Native UI components for iOS, Android and web
Article URL: https://leoanimesh.github.io/ferrule/ Comments URL: https://news.ycombinator.com/item?id=50411346 Points: 1 # Comments: 0
Show HN: LLM weight editing tool made by a 15-year-old
Article URL: https://github.com/Miyamoto-Renya/LaplacesScissors Comments URL: https://news.ycombinator.com/item?id=50409986 Points: 1 # Comments: 0
Show HN: Nino – Fastest C# serializer, safe on untrusted input, also TS/Go/Rust
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
Show HN: PicoDiet – compress images to a visual quality score, not a setting
Article URL: https://picodiet.com/ Comments URL: https://news.ycombinator.com/item?id=50411986 Points: 1 # Comments: 0
Show HN: SEC Fundamentals – free financial API
Article URL: https://www.quotelinks.com/api/sec-fundamentals/ Comments URL: https://news.ycombinator.com/item?id=50411013 Points: 1 # Comments: 0
b12182
The latest release of llama.cpp (v1.2182) includes a change to raise the compute view limit for buffers with few tensors in the meta backend, along with reverting the deepseek41 test model back to a 6-layer configuration. The release provides precompiled binaries for multiple platforms including macOS, Linux, Windows, Android, and iOS, supporting various backends such as CUDA, Vulkan, ROCm, OpenVINO, SYCL, and Snapdragon-specific hardware. Some openEuler builds remain disabled.
Copilot impact dashboard adds chat and VS Code agent usage
The Agent adoption chart in the Copilot impact dashboard now includes Copilot Chat and the VS Code Agents window, so you can see engagement across nine Copilot experiences. The adoption… The post Copilot impact dashboard adds chat and VS Code agent usage appeared first on The GitHub Blog.
How Greenlight Financial built an autonomous SOC solution on Amazon Bedrock AgentCore
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.
Show HN: GC2 (Geo Compute Cloud)
Article URL: https://gc2.io/ Comments URL: https://news.ycombinator.com/item?id=50404243 Points: 2 # Comments: 0
Show HN: One-script installer for TRELLIS.2 on Nvidia Blackwell under WSL2
Article URL: https://github.com/Creepybits/trellis2-blackwell-wsl Comments URL: https://news.ycombinator.com/item?id=50403233 Points: 1 # Comments: 0
Show HN: Realtime sync engine without any new infra
Tandem is an open-source sync engine and client-side database that plugs into your existing infrastructure. Add the Tandem library to sync data in real time across your clients and server, without deploying any new services.I started work on Tandem 6 years ago as a side project. I was frustrated by the sync engine landscape at the time, thinking I could build a better DX and require less lock-in. Even now, sync engine companies and “full-stack databases” need you to migrate to their database or provision a separate service with auth, networking, etc. This makes them annoying to maintain and also harder to adopt incrementally.Tandem is designed to just be embedded into your existing frontend and backend code. You define a schema, set up a database adapter (any SQL-like database is supported), add the new sync endpoint to your existing API, and then you're good to go.Tandem uses DST (Deterministic Simulation Testing) on every PR and nightly to ensure correctness. Our DST system Gatekeeper lets us model not only storage and network failures, but also race conditions, by gating each call between client/server/other services independently.Tandem currently supports any React-based frontend, any serverful backend (like Cloudflare Durable Objects), and any SQL-like database. New adapters are easy to build, and PRs and issues are appreciated!Open-source and MIT licensed: https://github.com/tanishqkancharla/tandem Comments URL: https://news.ycombinator.com/item?id=50398763 Points: 1 # Comments: 0
A Night at the LLM Zoo: Data Conditioned Flow Matching for Generating Pretrained LLM Weights
arXiv:2611.07103v1 Announce Type: new Abstract: A challenge to Weight Space Learning as it expands from task specific and vision models to Large Language Models (LLMs) is the absence of a well curated and analyzed LLM population. To address this, we train such a model zoo for application to Weight Space Generation by systematically varying the data mixture composition between web text, books, mathematics, coding, and French across populations of 51M, 124M, and 355M parameter GPT-2 Models, each tier with count N = 100. We analyze these populations by cosine similarity, functional diversity, Leave-One-Mixture-Out modeling, and variance decomposition. We show that the zoo contains diverse models whose variation is structured by training mixture composition. We embed the training mixture composition to condition a Flow Matching model of complete pretrained LLM weights in Gram-PCA compressed weight space. By demonstrating zero-shot generalization to held out mixtures, measuring Continuous Ranked Probability Score skill, as well as finding minimal changes in downstream test-set perplexity and general capability benchmark scores, we show that our generator generalizes on the simplex of data mixtures to produce models that perform similarly to those in the zoo ex nihilo. Finally, we produce all source code and artifacts to support the continued expansion of Weight Space Learning and Generation into the LLM domain.
Agents Are Going Async
Async subagents let an agent stay responsive while background workers run long tasks. How the orchestrator, notifications and approvals work in Deep Agents.