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Article URL: https://www.youtube.com/watch?v=hZyNFaojbew

Comments URL: https://news.ycombinator.com/item?id=47021880

Points: 1

# Comments: 0

Article URL: https://bernoff.com/blog/marching-morons-a-year-in-books-ai-character-names-newsletter-4-february-2026

Comments URL: https://news.ycombinator.com/item?id=47021879

Points: 1

# Comments: 0

Article URL: https://margaretstorey.com/blog/2026/02/09/cognitive-debt/

Comments URL: https://news.ycombinator.com/item?id=47021878

Points: 3

# Comments: 0

I'm a novice. I'm asking this question not to cause any argument. I need to set up an internal server for the company. Initially, there were 8 4T hard drives. In the future, depending on business growth, it may increase to 32 or more. As a novice, I learned some knowledge about storage pools on the Internet. Only two cases met the requirements. Softraid and LVM. However, it's impossible to determine which one is more suitable for my needs. Additionally, I noticed that both of these technologies haven't had major feature upgrades for a long time. They seem to be quite mature. So I sincerely would like to ask which of these two technologies is more suitable for me. Thank you very much. Regardless of which one you prefer, please don't argue. Thank you again.

Comments URL: https://news.ycombinator.com/item?id=47021868

Points: 1

# Comments: 0

Article URL: https://www.lennysnewsletter.com/p/engineers-are-becoming-sorcerers

Comments URL: https://news.ycombinator.com/item?id=47021857

Points: 1

# Comments: 0

Article URL: https://play.google.com/store/apps/details?id=com.companyname.ktrack&hl=en

Comments URL: https://news.ycombinator.com/item?id=47021849

Points: 1

# Comments: 0

Comments URL: https://news.ycombinator.com/item?id=47021821

Points: 1

# Comments: 0

Hi HN,

I built DocSync because every team I've worked on has the same problem: documentation that was accurate when it was written and never updated after.

DocSync uses tree-sitter to parse your code and extract symbols (functions, classes, types). On every commit, a pre-commit hook compares those symbols against existing docs. If you added a function without documenting it, the commit is blocked.

How it works:

1. `clawhub install docsync` (free) 2. `docsync generate .` — generates docs from your code 3. `docsync hooks install` — installs a lefthook pre-commit hook 4. From now on, every commit checks for doc drift

Key design decisions: - 100% local — no code leaves your machine. Uses tree-sitter for AST parsing, not an LLM. - Falls back to regex if tree-sitter isn't installed - Uses lefthook (not husky) for git hooks — it's faster and language-agnostic - License validation is offline (signed JWT, no phone-home) - Free tier does one-shot doc generation. Pro ($29/user/mo) adds hooks and drift detection.

Supports TypeScript, JavaScript, Python, Rust, Go, Java, C/C++, Ruby, PHP, C#, Swift, Kotlin.

Landing page: https://docsync-1q4.pages.dev

Would love feedback on the approach. Is doc drift detection something your team would actually use?

Comments URL: https://news.ycombinator.com/item?id=47021705

Points: 1

# Comments: 0

Article URL: https://www.youtube.com/watch?v=3_2NvGVl554

Comments URL: https://news.ycombinator.com/item?id=47021699

Points: 1

# Comments: 0

Article URL: https://overtype.dev/

Comments URL: https://news.ycombinator.com/item?id=47021698

Points: 1

# Comments: 0

Article URL: https://www.wired.com/story/made-in-china-niche-websites-are-seeing-a-surge-of-mysterious-traffic-from-china/

Comments URL: https://news.ycombinator.com/item?id=47021689

Points: 1

# Comments: 0

Show HN: AI-generated assembly vs GCC -O3 on real codebases (300K fuzz, 0 failures) Three kernels extracted from real open source projects, optimized with AI-generated x86-64 assembly, verified with 100K differential fuzz each: KernelAI strategySpeedupVerdictBase64 decodeSSSE3 pshufb table-free lookup4.8–6.3xAI winsLZ4 fast decodeSSE 16-byte match copy~1.05xAI wins (marginal)Redis SipHashReordered SIPROUND scheduling0.97xGCC wins The base64 win: GCC can't auto-vectorize a 256-byte lookup table (it's a gather pattern). The AI replaces it with a pshufb nibble trick — 16 parallel lookups in one instruction, zero table accesses. 1.8 GB/s → 11.6 GB/s. The SipHash loss: on pure ALU kernels (adds, rotates, XORs), GCC's scheduler is already near-optimal. 300K total fuzz iterations, zero mismatches. Every result is one command to reproduce.

Comments URL: https://news.ycombinator.com/item?id=47021641

Points: 1

# Comments: 1

Recently I've been learning about [LLM-powered ReAct architecture](https://arxiv.org/pdf/2210.03629) for designing software solutions. I've also worked on a high-scale production implementation of this approach, which got me thinking about all the applications I've built in the past.

Theoretically, any of them could be implemented using the ReAct architecture, but I'm still grappling with when this makes sense and when it doesn't. Some applications clearly benefit from LLM-backed reasoning—particularly those with heavy business logic that changes frequently. In these cases, updating prompts could replace code changes, potentially enabling product teams to directly influence system behavior without engineering involvement.

On the other hand, static data processing pipelines seem like poor candidates for this architecture. When the logic is stable and deterministic, the overhead and unpredictability of LLM inference doesn't add value. The sweet spot appears to be applications where business rules evolve rapidly and the cost of maintaining traditional code outweighs the complexity of prompt engineering.

https://www.sanathkandikanti.com/meditations

Comments URL: https://news.ycombinator.com/item?id=47021638

Points: 1

# Comments: 0

Article URL: https://www.iranintl.com/en/202602146821

Comments URL: https://news.ycombinator.com/item?id=47021623

Points: 1

# Comments: 0

Article URL: https://stackdiver.com/posts/cynthias-valentine/

Comments URL: https://news.ycombinator.com/item?id=47021619

Points: 1

# Comments: 0

Article URL: https://www.youtube.com/watch?v=1pPoB8be1G0

Comments URL: https://news.ycombinator.com/item?id=47021617

Points: 1

# Comments: 0

Article URL: https://worksonmymachine.ai/p/as-complexity-grows-architecture

Comments URL: https://news.ycombinator.com/item?id=47021603

Points: 1

# Comments: 0

Article URL: https://zamoshi.substack.com/p/ninety-four-degrees

Comments URL: https://news.ycombinator.com/item?id=47021586

Points: 2

# Comments: 0

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Comments URL: https://news.ycombinator.com/item?id=47021584

Points: 1

# Comments: 0

I couldn't get access to Project Genie 3, so I built a (very minified) version on top of LocalGPT, my local AI assistant in Rust. cargo install localgpt-gen — you describe a scene in natural language, the LLM generates it in Bevy. Early and rough, but it works: https://www.youtube.com/watch?v=n18qnSDmBK0 LocalGPT itself is a 38MB single-binary AI assistant with kernel-enforced shell sandboxing (Seatbelt/Landlock/seccomp) and HMAC-signed instruction files. localgpt-gen ships as a separate binary (>100MB) to keep the core small. Code: https://github.com/localgpt-app/localgpt Site: https://localgpt.app

Comments URL: https://news.ycombinator.com/item?id=47021572

Points: 1

# Comments: 0