Hacker News

Article URL: https://twitter.com/aidaxbaradari/status/2028864606568067491

Comments URL: https://news.ycombinator.com/item?id=47235620

Points: 3

# Comments: 1

Article URL: https://www.afox.dev/posts/the-attention-tax

Comments URL: https://news.ycombinator.com/item?id=47235600

Points: 1

# Comments: 0

Article URL: https://www.wired.com/story/gps-attacks-on-ships-spike-amid-the-us-and-israeli-war-on-iran/

Comments URL: https://news.ycombinator.com/item?id=47235591

Points: 1

# Comments: 0

In Math We Trust: The Nova Experiment In math, we falsify, 0.0001eth Litmus test for civilization Send 0.0001 ETH to the contract: 0xB0F4afDCeEc7757eec83183ab1F8cb42238DD48C and get the answer immediately.

Comments URL: https://news.ycombinator.com/item?id=47235585

Points: 1

# Comments: 0

Article URL: https://www.wsj.com/us-news/law/justice-department-seeks-to-reverse-course-and-defend-law-firm-sanctions-47fa3ba3

Comments URL: https://news.ycombinator.com/item?id=47235577

Points: 3

# Comments: 0

Article URL: https://www.washingtonpost.com/business/2026/03/02/katie-miller-solar-power-trump/

Comments URL: https://news.ycombinator.com/item?id=47235550

Points: 1

# Comments: 0

Hi HN,

I’m part of the team building Sniphi.

Sniphi is a modular digital nose that uses gas sensors and machine-learning models to convert volatile organic compound (VOC) data into a machine-readable signal that can be integrated into existing QA, monitoring, or automation systems. The system is currently in an R&D phase, but already exists as working hardware and software and is being tested in real environments.

The project grew out of earlier collaborations with university researchers on gas sensors and odor classification. What we kept running into was a gap between promising lab results and systems that could actually be deployed, integrated, and maintained in real production environments.

One of our core goals was to avoid building a single-purpose device. The same hardware and software stack can be trained for different use cases by changing the training data and models, rather than the physical setup. In that sense, we think of it as a “universal” electronic nose: one platform, multiple smell-based tasks.

Some design principles we optimized for: - Composable architecture: sensor ingestion, ML inference, and analytics are decoupled and exposed via APIs/events - Deployment-first thinking: designed for rollout in factories and warehouses, not just controlled lab setups - Cloud-backed operations: model management, monitoring, updates run on Azure, which makes it easier to integrate with existing industrial IT setups - Trainable across use cases: the same platform can be retrained for different classification or monitoring tasks without redesigning the hardware

One public demo we show is classifying different coffee aromas, but that’s just a convenient example. In practice, we’re exploring use cases such as: - Quality control and process monitoring - Early detection of contamination or spoilage - Continuous monitoring in large storage environments (e.g. detecting parasite-related grain contamination in warehouses)

Because this is a hardware system, there’s no simple way to try it over the internet. To make it concrete, we’ve shared: - A short end-to-end demo video showing the system in action (YouTube) - A technical overview of the architecture and deployment model: https://sniphi.com/

At this stage, we’re especially interested in feedback and conversations with people who: - Have deployed physical sensors at scale - Have run into problems that smell data might help with - Are curious about piloting or testing something like this in practice

We’re not fundraising here. We’re mainly trying to learn where this kind of sensing is genuinely useful and where it isn’t.

Happy to answer technical questions.

Comments URL: https://news.ycombinator.com/item?id=47234907

Points: 1

# Comments: 0

I built a new programming language for AI & Data - 'ThinkingLanguage' in 5 days capable of transferring 1 Billion rows in 30 seconds.

Every data team runs the same stack: Python for glue code, SQL for transforms, Spark or dbt for scale, YAML for orchestration. Four languages, four mental models, four places for bugs.

What if one language could do it all?

ThinkingLanguage (TL) is a purpose-built language for Data Engineering and AI. The pipe operator is a first-class citizen. Tables, schemas, filters, joins, and aggregations are native - not library calls.

let users = read_csv("users.csv")

users |> filter(age > 30) |> join(orders, on: id == user_id) |> aggregate(by: name, total: sum(amount)) |> sort(total, "desc") |> show()

What's under the hood: - Apache Arrow columnar format - DataFusion query engine with lazy evaluation and automatic optimization - CSV, Parquet, and PostgreSQL connectors - 1M rows filtered + aggregated + sorted in 0.3 ms - Written in Rust

Includes a JIT compiler (Cranelift/LLVM), native AI/ML operations (train, predict, embed), streaming pipelines with Kafka, GPU (CUDA, ROCm). Python FFI Bridge (Run/Call Python Libraries) and a full ecosystem with notebooks and a package registry.

Download via npx, ssh native installer, crates, github

This is open source (Apache Licence). If you're a data engineer tired of context-switching between five tools, or a Rust developer who wants to contribute to something new - check it out the github repo: https://github.com/mplusm/thinkinglanguage

Data Deserves its own language.

Comments URL: https://news.ycombinator.com/item?id=47234906

Points: 1

# Comments: 0

Article URL: https://www.theeducatoronline.com/k12/news/its-time-schools-moved-beyond-passwords-expert-says/288825

Comments URL: https://news.ycombinator.com/item?id=47234895

Points: 1

# Comments: 0

Article URL: https://lilaq.org/

Comments URL: https://news.ycombinator.com/item?id=47234890

Points: 1

# Comments: 0

Article URL: https://www.youtube.com/watch?v=dMZ5a0lgQKA

Comments URL: https://news.ycombinator.com/item?id=47234883

Points: 1

# Comments: 0

Article URL: https://www.theregister.com/2026/03/03/brit_games_studio_cloud_imperium/

Comments URL: https://news.ycombinator.com/item?id=47234882

Points: 1

# Comments: 0

Article URL: https://grith.ai/blog/agent-skills-supply-chain

Comments URL: https://news.ycombinator.com/item?id=47234881

Points: 1

# Comments: 0

Article URL: https://nousresearch.com/hermes-agent/

Comments URL: https://news.ycombinator.com/item?id=47234855

Points: 1

# Comments: 0

Article URL: https://docs.cloud.google.com/vertex-ai/generative-ai/docs/models/gemini/3-1-flash-lite

Comments URL: https://news.ycombinator.com/item?id=47234849

Points: 1

# Comments: 1

Article URL: https://www.simular.ai/sai

Comments URL: https://news.ycombinator.com/item?id=47234838

Points: 1

# Comments: 0

Noticed this when trying to download claude code. The claude.ai domain is currently not reliably resolving from many major sources (1.1.1.1, 8.8.8.8, 9.9.9.9). The same appears to be true for other .ai domains. Is there something going on with the Anguilla nameservers?

Comments URL: https://news.ycombinator.com/item?id=47234828

Points: 1

# Comments: 0

Article URL: https://hornet.dev/blog/the-context-window-is-not-your-database

Comments URL: https://news.ycombinator.com/item?id=47234781

Points: 2

# Comments: 0

Article URL: https://www.bleepingcomputer.com/news/security/lexisnexis-confirms-data-breach-as-hackers-leak-stolen-files/

Comments URL: https://news.ycombinator.com/item?id=47234764

Points: 2

# Comments: 0

Hey HN, I'm Ravi, and I've spent the last few months building *SysNav* (https://sysnav.ai). It's an intelligent terminal workspace designed specifically for SREs who want the speed of a native shell but the context-awareness of an AI when things break.

**The Problem** I found myself constantly context-switching: run `kubectl`, see error, copy to LLM, sanitize PII manually, paste, get generic fix, modify for my variables... it was friction-heavy. **The Solution: SysNav** I built SysNav as a "Assistant for DevOps and Ops". It wraps your terminal in a native "Intelligent Cockpit" interface: 1. **Ask Mode (Safe)**: You ask "Why is the redis pod crashing?". SysNav inspects the screen output (using a local PTY buffer), anonymizes PII, and sends *only* the context to the LLM. It returns a suggested command. **It creates a human-in-the-loop safety barrier.** 2. **Agent Mode (Autonomous)**: If you trust it, you can give it a goal ("Find all zombie processes and kill them"), and it will execute the steps one by one, stopping if it encounters an unexpected error. **Security Architecture (The "Paranoia" Check)** This was the most important part for me. SysNav uses a **Local-First Architecture**: * **SSH Keys & Env Vars**: Never leave your machine. They are stored in your release of the Electron app. * **Context**: We only send the text buffer of the terminal (logs/errors) to the inference layer. We have a redact-pii step that runs locally before network requests. **Status** We are currently in **Public Beta**. The core terminal is "fast-enough" (<100ms startup), and the AI context awareness is saving me about 30 minutes per incident. I'd love for you to roast the architecture or tell me what features would make you actually trust an AI in your production environment. Check it out: https://sysnav.ai (Download available for macOS/Linux/Windows)

Comments URL: https://news.ycombinator.com/item?id=47234763

Points: 1

# Comments: 0