Feed aggregator

Article URL: https://fulcrumresearch.ai/2026/03/05/more-is-different-for-intelligence.html

Comments URL: https://news.ycombinator.com/item?id=47282846

Points: 2

# Comments: 0

Article URL: https://www.simplenews.ai/news/poet-x-enables-billion-parameter-llm-training-on-single-h100-gpu-ktw3

Comments URL: https://news.ycombinator.com/item?id=47282516

Points: 1

# Comments: 0

Hi HN,

I built Habit Online Daily Tracker, a simple Android app for tracking daily habits and staying consistent.

The idea was to create a very straightforward habit tracker with a clean interface and no unnecessary complexity. I wanted something where you can quickly see your habits for the day, mark them as complete, and track your progress over time.

Main features: - Habit dashboard showing all active habits - Daily completion tracking and streaks - Profile with long-term stats - Customizable notifications and settings

The goal is to make daily progress visible so small habits can compound over time.

I would really appreciate feedback on the UX, features, or anything that could make it more useful.

Comments URL: https://news.ycombinator.com/item?id=47282509

Points: 1

# Comments: 0

Hi HN,

I've been working on a project called Kaeso.

The idea started fairly simple: I wanted to explore infrastructure around AI agents. While building it, the concept slowly evolved into something more focused on integrations.

One problem that keeps appearing when building agent systems is connecting them to real services in a structured and secure way. Every system ends up implementing its own integrations with tools like Google, Slack, GitHub, etc.

Kaeso is an attempt to experiment with a unified connection layer where services can be connected once and then accessed by agents through a consistent interface.

While building the project, the original idea changed quite a bit, which I wrote about here:

https://kaeso.ai/blog/redefining-kaeso

The platform is still early, but I'm curious what people here think about the idea of infrastructure specifically designed for AI agents.

Feedback is very welcome.

Comments URL: https://news.ycombinator.com/item?id=47282502

Points: 1

# Comments: 0

I am a 40+-year-old-slightly-techie-middle-aged-man who occasionally writes code to make life easier. I was a developer once - a very long time ago. I am an Engineer by degree and my instinct goes for solution. I work in tech - but on the "_request_ developers what to build" side, not the "actually build it" side. With AI, I am now able to build more.

So I built WebBridge (yes - not so fancy name there). (Well - Claude built it. I directed. Like every PM does.)

What it actually does:

1. You install a Chrome extension 2. You browse to a site you're logged into - your library, your lab results portal, whatever 3. You click Record, do the thing you want to automate, click Stop 4. Claude reads the captured API traffic and generates a permanent MCP server 5. That server works with any MCP client - Claude (Cowork/Code), Cursor, VS Code, Windsurf, Cline, you name it

The whole thing takes about 10 minutes. No code written by you.

This is for non-tech folks who live inside AI providers, who just want to use it and move on. Legal analysts, market researchers, market watchers, marketing and competitive intelligence, and anyone who wants to use a specific website for a specific purpose, repeatedly.

The README has some use cases showcased: "Public Library Search" and "Legal Compliance Auditing."

There may not be an exact equivalent anywhere to what I purpose-built. I'd welcome being proven wrong on that.

Feedback is welcome - that's why I'm posting.

Comments URL: https://news.ycombinator.com/item?id=47282494

Points: 1

# Comments: 0

Article URL: https://simplex.chat/why/

Comments URL: https://news.ycombinator.com/item?id=47282485

Points: 1

# Comments: 0

Article URL: https://phys.org/news/2026-02-crowning-protein-jellyfish-green-scientists.html

Comments URL: https://news.ycombinator.com/item?id=47282475

Points: 1

# Comments: 0

I built this after watching teams deploy autonomous agents with zero accountability. No verified identity, no audit trail, no circuit breakers.

MultiPowerAI is the trust layer the agent web needs:

- Cryptographic agent identity + trust scoring (<200ms) - Behavioral circuit breakers (auto-suspend on anomaly) - Human approval queues for high-stakes actions - Full cryptographic audit trail (every action signed + timestamped) - Skills marketplace (agents buy/sell capabilities, sellers keep 80%) - 5-model consensus: Claude + GPT + Gemini + DeepSeek in one API call

Free tier available. Would love feedback from anyone building production agent systems.

Comments URL: https://news.ycombinator.com/item?id=47282453

Points: 1

# Comments: 0

Article URL: https://www.tmdevlab.com/mcp-server-performance-benchmark.html

Comments URL: https://news.ycombinator.com/item?id=47282451

Points: 1

# Comments: 0

- llm-stream — streaming from OpenAI + Anthropic, callback-based - llm-cache — file-backed semantic cache, LRU eviction - llm-cost — offline token counting + cost estimation - llm-retry — exponential backoff + circuit breaker + provider failover - llm-format — structured output enforcer with hand-rolled JSON parser

Drop in one .hpp, link libcurl, done. No nlohmann, no boost, no Python.

https://github.com/Mattbusel/llm-stream https://github.com/Mattbusel/llm-cache https://github.com/Mattbusel/llm-cost https://github.com/Mattbusel/llm-retry https://github.com/Mattbusel/llm-format

Comments URL: https://news.ycombinator.com/item?id=47282433

Points: 1

# Comments: 0

Article URL: https://openguard.sh

Comments URL: https://news.ycombinator.com/item?id=47282426

Points: 1

# Comments: 0

When I was learning French, and now Russian, I constantly had the same frustrating problem: I could listen to podcasts and not understand what was being said. Especially in French, words blended together. I tried everything from pausing and rewinding episodes to using Google Translate. But more often than not, I gave up because that was killing my motivation. Eventually, I decided to solve the problem myself. I built PodcastsToText, a freemium tool that lets you paste any podcast episode link from Spotify or Apple Podcasts and transcribe it automatically (up to 30 minutes for free). Now, instead of struggling to catch every word, I can focus on learning, reread the transcript if I miss something, and really internalize the language. I hope this helps others like me who’ve spent hours listening but still feel lost. Feedback and suggestions are welcome!

Comments URL: https://news.ycombinator.com/item?id=47282422

Points: 1

# Comments: 0

Article URL: https://arxiv.org/abs/2509.04664

Comments URL: https://news.ycombinator.com/item?id=47282421

Points: 1

# Comments: 0

Comments URL: https://news.ycombinator.com/item?id=47282415

Points: 2

# Comments: 1

Article URL: https://writings.stephenwolfram.com/2013/06/there-was-a-time-before-mathematica/

Comments URL: https://news.ycombinator.com/item?id=47282402

Points: 2

# Comments: 1

Commentary: The mobile industry doesn't suffer from a lack of innovation, but from a lack of mass adoption of fresh designs.

Article URL: https://www.justice.gov/usao-md/pr/russian-ransomware-administrator-pleads-guilty-wire-fraud-conspiracy

Comments URL: https://news.ycombinator.com/item?id=47282393

Points: 1

# Comments: 0

Hi HN,

I’ve been building a Rust-first backtesting engine for limit order book strategies and just open sourced the core engine.

Repo: https://github.com/chasemetoyer/Backtesting-Engine

The goal was to build something closer to how exchanges actually behave than typical OHLC-based backtesting frameworks.

Key features:

• L3 limit order book replay • deterministic event-driven matching engine • FIFO queue position modeling • Python strategy bindings for research workflows • parquet ingestion for high-volume datasets • replay trace tools for debugging strategy behavior

The core engine is written in Rust and exposed to Python via maturin. The idea is to combine Rust performance with Python-based research workflows.

Typical workflow:

1) Convert raw exchange data (ex: CoinAPI LIMITBOOK files) into canonical engine parquet 2) Run deterministic replay through the Rust engine 3) Execute strategies through Python bindings 4) analyze fills, equity curves, and risk metrics

The repo currently includes several example microstructure strategies such as:

• queue imbalance scalper • flow microprice scalper • cumulative flow momentum

I built this mainly to experiment with order book strategies where queue position and microstructure actually matter.

Would love feedback from people working on:

• market microstructure research • HFT simulation • Rust systems engineering • trading infrastructure

Especially interested in ideas for improving:

• event replay throughput • strategy interface design • multi-asset simulation

Thanks!

Comments URL: https://news.ycombinator.com/item?id=47282392

Points: 1

# Comments: 0