Feed aggregator

Article URL: https://www.nytimes.com/2026/02/24/technology/taiwan-china-chips-silicon-valley-tsmc.html

Comments URL: https://news.ycombinator.com/item?id=47133069

Points: 4

# Comments: 1

Article URL: https://www.theregister.com/2026/02/23/open_source_devs_column/

Comments URL: https://news.ycombinator.com/item?id=47133062

Points: 2

# Comments: 0

Article URL: https://github.com/GreatScott/enveil

Comments URL: https://news.ycombinator.com/item?id=47133055

Points: 2

# Comments: 0

Article URL: https://old.reddit.com/r/selfhosted/comments/1rckopd/huntarr_your_passwords_and_your_entire_arr_stacks/

Comments URL: https://news.ycombinator.com/item?id=47133052

Points: 1

# Comments: 0

Article URL: https://danielmiessler.com/blog/why-you-should-hate-anthropic

Comments URL: https://news.ycombinator.com/item?id=47133031

Points: 3

# Comments: 0

Hey everyone,

I’ve been working on a project called L88 — a local RAG system that I initially focused on UI/UX for, so the retrieval and model architecture still need proper refinement.

Repo: https://github.com/Hundred-Trillion/L88-Full

I’m running this on 8GB VRAM and a strong CPU (128GB RAM). Embeddings and preprocessing run on CPU, and the main model runs on GPU. One limitation I ran into is that my evaluator and generator LLM ended up being the same model due to compute constraints, which defeats the purpose of evaluation.

I’d really appreciate feedback on:

Better architecture ideas for small-VRAM RAG

Splitting evaluator/generator roles effectively

Improving the LangGraph pipeline

Any bugs or design smells you notice

Ways to optimize the system for local hardware

I’m 18 and still learning a lot about proper LLM architecture, so any technical critique or suggestions would help me grow as a developer. If you check out the repo or leave feedback, it would mean a lot — I’m trying to build a solid foundation and reputation through real projects.

Thanks!

Comments URL: https://news.ycombinator.com/item?id=47133027

Points: 1

# Comments: 0

Article URL: https://thunderseethe.dev/posts/compiler-education-deserves-a-revoluation/

Comments URL: https://news.ycombinator.com/item?id=47133014

Points: 2

# Comments: 0

Article URL: https://www.phoronix.com/news/Torvalds-Unseeded-Random

Comments URL: https://news.ycombinator.com/item?id=47133004

Points: 2

# Comments: 0

Comments URL: https://news.ycombinator.com/item?id=47132771

Points: 1

# Comments: 0

Serious question.

Outside government or heavily regulated enterprise, what is Microsoft’s core value prop in 2026?

It feels like a lot of adoption is inherited — contracts, compliance, enterprise trust, existing org gravity. Not necessarily technical preference.

If you were starting from scratch today with no legacy, no E5 contracts, no sunk cost — how many teams would actually choose the full MS stack over best-of-breed tools?

Curious what people here have actually chosen in greenfield builds.

Comments URL: https://news.ycombinator.com/item?id=47132766

Points: 1

# Comments: 0

Article URL: https://platia.ai/

Comments URL: https://news.ycombinator.com/item?id=47132761

Points: 1

# Comments: 1

Article URL: https://www.businessinsider.com/ibm-stock-price-anthropic-ai-update-cobol-language-software-selloff-2026-2

Comments URL: https://news.ycombinator.com/item?id=47132740

Points: 2

# Comments: 0

Article URL: https://www.cnbc.com/2026/02/23/uber-acquiring-spothero.html

Comments URL: https://news.ycombinator.com/item?id=47132737

Points: 1

# Comments: 0

Hi HN!

Most “chat with PDF” tools work fine until you try using them for something that actually matters, like contracts.

The issue isn’t that they can’t answer questions. It’s that you can’t trust the answers. They return something that sounds correct, but don’t clearly show where it came from, or they miss context from referenced clauses and related documents.

Legal docs make this harder because questions aren’t uniform: - sometimes you’re searching concepts - sometimes exact clause IDs - sometimes text from a different linked document

Most systems handle all of those the same way, which is where things break.

So I built LexReviewer, an open-source backend designed around a single rule: ""an answer isn’t useful unless you can verify it instantly.""

Instead of treating every query identically, it adapts its search strategy based on what you’re asking and can follow references across documents when needed. The result is answers that stay grounded in real text and point directly to the source passage.

Repo: https://github.com/LexStack-AI/LexReviewer

-- Currently tested on 300+ page contracts with cross-references

Feedback I’d especially value:

- Where do current document-AI systems fail hardest for you? - What’s been the biggest blocker to trusting AI outputs in production workflows? - If you’ve built something similar, what design choices ended up mattering most?

Comments URL: https://news.ycombinator.com/item?id=47132730

Points: 1

# Comments: 1

Article URL: https://longform.asmartbear.com/strategic-choices/

Comments URL: https://news.ycombinator.com/item?id=47132728

Points: 1

# Comments: 0

Article URL: https://blog.ptidej.net/debug-chair/

Comments URL: https://news.ycombinator.com/item?id=47132723

Points: 1

# Comments: 0

Article URL: https://www.intel.com/content/www/us/en/download/785597/intel-arc-graphics-windows.html

Comments URL: https://news.ycombinator.com/item?id=47132721

Points: 2

# Comments: 0

Article URL: https://railway.com/stats

Comments URL: https://news.ycombinator.com/item?id=47132712

Points: 1

# Comments: 0

Article URL: https://www.youtube.com/watch?v=fT7Zi_xF8Ts

Comments URL: https://news.ycombinator.com/item?id=47132707

Points: 1

# Comments: 0