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Background: I've been working on agentic guardrails because agents act in expensive/terrible ways and something needs to be able to say "Maybe don't do that" to the agents, but guardrails are almost impossible to enforce with the current way things are built.

Context: We keep running into so many problems/limitations today with MCP. It was created so that agents have context on how to act in the world, it wasn't designed to become THE standard rails for agentic behavior. We keep tacking things on to it trying to improve it, but it needs to die a SOAP death so REST can rise in it's place. We need a standard protocol for whenever an agent is taking action. Anywhere.

I'm almost certainly the wrong person to design this, but I'm seeing more and more people tack things on to MCP rather than fix the underlying issues. The fastest way to get a good answer is to submit a bad one on the internet. So here I am. I think we need a new protocol. Whether it's AAP or something else, I submit my best effort.

Please rip it apart, lets make something better.

Comments URL: https://news.ycombinator.com/item?id=47235632

Points: 2

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

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

Points: 3

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

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

Points: 1

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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

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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

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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

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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

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Get ready for new movies like Peaky Blinders: The Immortal Man, War Machine, BTS: The Return and more arriving this month.

We've put more than a dozen blenders to the test to see which countertop counterparts came out on top.

The Security Think Tank looks at platformisation considering questions such as how CISOs can distinguish between a truly integrated platform and 'integration theater, and how to protect unified platforms.

Cynomi, Hopami, Mimiu, Mogogo and Morin just want to be loved. Is that so wrong?

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

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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

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Article URL: https://lilaq.org/

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

Points: 1

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

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

Points: 1

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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

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

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

Points: 1

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

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

Points: 1

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