Feed aggregator

Article URL: https://sidepulse.io/

Comments URL: https://news.ycombinator.com/item?id=49605250

Points: 1

# Comments: 0

Article URL: https://danluu.com/agentic-testing/

Comments URL: https://news.ycombinator.com/item?id=49605246

Points: 1

# Comments: 0

Article URL: https://sjg.io/writing/ai-is-making-us-build-too-much/

Comments URL: https://news.ycombinator.com/item?id=49605245

Points: 1

# Comments: 0

Article URL: https://jackalope.dev/

Comments URL: https://news.ycombinator.com/item?id=49605236

Points: 1

# Comments: 0

Article URL: https://github.com/sysdev-0/grubst

Comments URL: https://news.ycombinator.com/item?id=49605229

Points: 1

# Comments: 0

Article URL: https://www.youtube.com/watch?v=9EuNUe-CJRM

Comments URL: https://news.ycombinator.com/item?id=49605222

Points: 1

# Comments: 0

Article URL: https://www.popularmechanics.com/science/environment/a73385264/china-reforesting-changes-hydrology-study/

Comments URL: https://news.ycombinator.com/item?id=49605207

Points: 1

# Comments: 0

Article URL: https://arxiv.org/abs/2608.14808

Comments URL: https://news.ycombinator.com/item?id=49605190

Points: 1

# Comments: 0

Article URL: https://tracker.archiveteam.org/

Comments URL: https://news.ycombinator.com/item?id=49605182

Points: 1

# Comments: 0

This tool started as a research for me trying to answer the question "Does AI agents drift from your architecture conventions or if you need enforcement instead?". So I started out building a benchmark to test that premise and on the scope I tested, the agents did not drift and I had to stop that research line. So to reuse what I have learnt from that research, I started out trying to catch engineer's regressions through review and CI and create those conventions codebases already follows with the evidence instead of leaving them in someone's head.

Before I started building this tool, I also mostly write the rules by hand and linter's documentations for every linter I wanted to use. But after my research, the tool automates the process and I'm excited to see how it can help other engineers too. I ran the full package workflow (scan, recommend) across 92,861 real public TypeScript repositories on github and it completed on every one without a single crash and the reversible install/uninstall round-trip held. I will appreciates any feedback you have on the rules it infers for your repo and how it can help you enforce them or not.

The study is public here https://github.com/Tommkruix/agentrulebench and Archprint is public here https://github.com/Tommkruix/archprint also.

Comments URL: https://news.ycombinator.com/item?id=49605147

Points: 1

# Comments: 0

Article URL: https://albertjoseph0.github.io/nyc-maptap/

Comments URL: https://news.ycombinator.com/item?id=49605122

Points: 2

# Comments: 1

People use embedding models all the time for rag/semantic retrieval. However, when a newer, more desireable model comes out, there is an expensive (both in time and computational) cost of re-embedding every document in the database.

However, I figured out an interesting way to forgo that upfront embedding cost.

algo:

old model/index -> retrieve top-K docs -> score those docs with the new model -> cache/materialize the new embeddings

so instead of rebuilding the entire vector store upfront, the old index keeps getting retrieved from, while the new model reranks those candidates.

This works surprisingly well for some model pairs, (i tested 63 source-> target migrations on h100s, on upto 1M documents).

For example, on a 1M document Natural Questions dataset,

native Qwen3-Embedding-8B: 0.6812 nDCG@10 Qwen3-4B -> Qwen3-8B, K=50: 0.6816 Qwen3-0.6B -> Qwen3-8B, K=50: 0.6638 MiniLM -> Qwen3-8B, K=50: 0.6486

(the hard part is determining k, I held the k constant above to give some sense of migratability).

You can install it with pip

pip install embedflow

and the code is on github

https://github.com/arnsri33/embedflow

Comments URL: https://news.ycombinator.com/item?id=49605110

Points: 1

# Comments: 0

Article URL: https://medium.com/freedomofthought/defeating-antisemitism-through-zionism-6c648c34c408

Comments URL: https://news.ycombinator.com/item?id=49605086

Points: 1

# Comments: 0

I’ve been working on Caveat, a self-hosted publishing tool for people who want control over their newsletter’s design, subscriber data, and infrastructure.

It includes a rich-text editor, autosave, reusable content blocks, publication designs, typography and colour controls, subscriber management, exports, and Resend email delivery. It includes both the editor and public website.

Repo: https://github.com/CaveatJS/site Examples/docs: https://caveat-home.vercel.app/

I built the first version over a few hours across three sessions with GPT-6 Astra. I chose the features, tested the workflows, and refined the design and implementation.

The sessions processed roughly 43.6M tokens, about 97% cached input.

Caveat is still early: paid subscriptions, scheduling, and multi-author workflows are not implemented yet.

If you publish a newsletter, what part of your workflow would prevent you from using it today?

Comments URL: https://news.ycombinator.com/item?id=49605072

Points: 1

# Comments: 0

Article URL: https://www.scmp.com/news/china/diplomacy/article/3366477/canada-and-china-thaw-continues-militaries-hold-first-talks-8-years-amid-us-pressure

Comments URL: https://news.ycombinator.com/item?id=49605046

Points: 6

# Comments: 2

Article URL: https://en.wikipedia.org/wiki/Thames_Tunnel

Comments URL: https://news.ycombinator.com/item?id=49605036

Points: 1

# Comments: 0

Article URL: https://www.passkeycentral.org/design-guidelines/optional-patterns/create-passkey-after-account-recovery-due-to-forgotten-password

Comments URL: https://news.ycombinator.com/item?id=49605024

Points: 1

# Comments: 0

Article URL: https://pmc.ncbi.nlm.nih.gov/articles/PMC11585964/

Comments URL: https://news.ycombinator.com/item?id=49605011

Points: 2

# Comments: 0

AI pink doesn’t Columbus get eleven this, yellow do beach you? Do more advanced model understand the above or is it only for humans?

Comments URL: https://news.ycombinator.com/item?id=49604630

Points: 1

# Comments: 0

Article URL: https://github.com/agentdesktop-dev/agentdesktop

Comments URL: https://news.ycombinator.com/item?id=49604626

Points: 1

# Comments: 1