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Originally, I am .NET software engineer working corporate job with microservices on RMQ and REST APIs. But I always had the desire to experiment with some different tech on the side.

After my friend shared his plans of helping facilitate medical conference which was bilingual and some of the audience members might feel missed out due to English not being their first language, I thought it's a nice opportunity to create a tool for it. It was an opportunity to work with audio, ASR, accessibility and websockets. It's something new for me.

Another inspiration was Formula 1 captions which I noticed during their broadcasts, which are generated in real-time. After digging a bit into underlying technology I thought to create something similar for event organisers and AV teams. Not as a replacement for human interpreters, but as a more accessible alternative to quite costly hardware options.

That's how Stage Captions was built. Audio comes from browser, gets streamed via WSS to backend, goes through processing pipeline and then captions are streamed back to viewers. Those captions can be displayed on venue TVs, opened on attendees devices by scanning QR code or incorporated into a broadcast using OBS for example.

Obviously, ASR and MT models are third party and not created by me, but there were still some fun challenges like scaling websockets (it was trickier than just adding one more container on load), audio processing (finding optimal chunking and buffering approach) and obviously stabilization algorithms for partial transcription / translation results.

As a developer I was happy to experiment with tech stack which is different from my daily work. And as a human it felt right to build something which can make in-person events easier to follow for non-native speakers and deaf/hard of hearing attendees.

Comments URL: https://news.ycombinator.com/item?id=49709975

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Article URL: https://ddropattack.eu/

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Article URL: https://doi.org/10.1111/acps.13794

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Article URL: https://www.astralcodexten.com/p/king-ludd

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Article URL: https://www.amytis.io/blog/insights-on-ai-from-the-reformation

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Article URL: https://proofsandprompts.com/2026/09/10/open-letter-about-the-mathathon/

Comments URL: https://news.ycombinator.com/item?id=49709882

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Article URL: https://crons.darcode.dev

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Article URL: https://freshteapot.net/writing/creating-word-audio-with-ai/

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Article URL: https://www.militarytimes.com/news/your-military/2026/09/14/us-air-force-secretary-reveals-deployment-of-on-orbit-space-control-weapons/

Comments URL: https://news.ycombinator.com/item?id=49709812

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Article URL: https://www.reddit.com/r/LocalLLaMA/comments/1wgii3h/deepseek_engineer_relections_on_rsi_burying_my/

Comments URL: https://news.ycombinator.com/item?id=49709746

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An agent that only reads and writes code is guessing whether its changes work. To maintain many projects I need one that can boot the app, run migrations, hit the page and read the actual error.

So I built Knecht. A Self-hostable tool. You connect your GitHub repos and it starts each project in an isolated Docker sandbox, generated with DDEV. Inside, an agent can do what a developer does locally. Reproduce a bug, run migrations, open the page in the browser etc.

Workflows can be started by triggers (Jira tickets, GitHub issues, PRs..) and can run across all projects at once, mixing AI agent steps with deterministic steps, similar to n8n.

Knecht is in beta and I am looking for people who maintain many projects. Works best with DDEV projects. https://github.com/knecht-works/knecht-cloud https://knecht.works/updates/beta-tester

Comments URL: https://news.ycombinator.com/item?id=49709714

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Article URL: https://jensrantil.github.io/posts/challenging-ai/

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Article URL: https://self.cv/en

Comments URL: https://news.ycombinator.com/item?id=49709695

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Article URL: https://www.nytimes.com/2026/09/12/us/influencers-social-media-algorithm.html

Comments URL: https://news.ycombinator.com/item?id=49709688

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Hi,

I wanted to share a project I've building for a while. QuantSupport is a quant library that comtemplates curve construction, simulation and risk analytics for a different asset classes (equity, rates, fx, FI).

https://github.com/jmelo11/quantsupport

It's goal is to be a realistic production tool for financial institutions, instead of being another analytics library as many other tools are. For this, it leverages automatic differentiation from the ground up to obtain risk functionalities (sensitivites), essential for real trading setups.

The library features other engines, like and XVA engine and a scripting language for structuring more complex payoff structures.

Also, its available an example in the following repo:

https://github.com/jmelo11/derivative_structuring

where LLMs leverage the scripting functionalities of the library to provide pricing and insights to user prompted requests, allowing model-consitent results and showing the flexibility of the library.

I like to think that is reaching a more mature state, but to know for sure, it would be very helpful to get feedback from people from the quant space. Any feedback is highly appreciated.

Comments URL: https://news.ycombinator.com/item?id=49709680

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Back Me Up is a tiny science-ish machine that finds published papers that back your argument. It stemmed from a conversation with a friend around life sciences, where we kind of landed at "surely there’s a paper confirming most arguments", i.e. coffee is bad, coffee is great, etc.

You enter a claim, e.g. "drinking wine is good for the heart" -> the system does a semantic search across a corpus (PMC, bioRxiv, medRxiv and arXiv) -> pulls in relevant quotes if they fit your claim, and gives you a verdict.

Frontend was done with a combination of Sol + Opus, I spent a bit of time exploring different designs and landed on this slightly ridiculous printer that spits out your evidence.

Some notes: - I have no accuracy evals, this was a throwaway thing I built, so no clue on reliability. It does pass the vibe test, so give it a whirl! - Initially built a RAG for a tiny set of PMC, but scaling that up seemed like a lot of work for an evening project. Luckily found an API that does the semantic search across the different sources.

Comments URL: https://news.ycombinator.com/item?id=49709678

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Article URL: https://skyforceui.com/

Comments URL: https://news.ycombinator.com/item?id=49709677

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Article URL: https://ethfata.com/

Comments URL: https://news.ycombinator.com/item?id=49709672

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What started as a joke (as I had the domain) turned into an MVP. Usefulness is... so far, questionable? But there are use cases where this kind of spec could come in handy.

My own spec lives at https://spec.md/allan. Add .md to the end and you will get the markdown version. Shareable with AI agents to give them context about you or the people you work with.

Feedback welcome!

Comments URL: https://news.ycombinator.com/item?id=49709631

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Article URL: https://mceglowski.substack.com/p/trying-to-understand-freedom

Comments URL: https://news.ycombinator.com/item?id=49709625

Points: 2

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