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Article URL: https://argybargy.dev Comments URL: https://news.ycombinator.com/item?id=48615609 Points: 2 # Comments: 0

Comments URL: https://news.ycombinator.com/item?id=48615488 Points: 5 # Comments: 1

Article URL: https://FareedKhan-dev.github.io/train-llm-from-scratch/ Comments URL: https://news.ycombinator.com/item?id=48615416 Points: 1 # Comments: 0

Article URL: https://github.com/adlternative/agent-historian Comments URL: https://news.ycombinator.com/item?id=48615257 Points: 1 # Comments: 0

Article URL: https://swiftalerts.trade/the-cadence-trade-hn Comments URL: https://news.ycombinator.com/item?id=48615059 Points: 1 # Comments: 0

<p>The moment you connect an MCP server, your coding agent stops being a thing that reads and writes in your repo and becomes a thing that can reach out and act. Read a database, hit an API, touch a service, pull in a web page. That's the entire appeal. It's also the entire problem, and the two are the same feature seen from two sides.</p> <p>I went through this wiring up tools for my own plugin work, and the thing that saved me from a worse mistake was a scar I already had. I'd shipped an AI ch

<p>At a conference this week, a VP at PingCAP (the company behind TiDB) dropped a line that's been rattling around my head ever since:</p> <blockquote> <p><strong>"Coding is over, Software is not."</strong></p> </blockquote> <p>Writing code is getting solved by LLMs, fast. But shipping <em>complex software into an enterprise</em> is still hard — and the hard part was never the code. It's permissions, memory, collaboration, security, context. That half didn't get easier. That half is the whole ga

A very simple idea: when you eat more than your maintenance calories, you gain weight; when you eat less than your maintenance calories, you lose weight. By using an algorithm, we can accurately figure out your maintenance calories more accurately than traditional regression based formulas like katch mc ardle. It's way more accurate than calorie burn tracking devices like fitness bands and watches. (garmin/apple watch/whoop etc...) Traditionally, people often use static TDEE calculators which of

Article URL: https://github.com/ethanhq/cc-fleet Comments URL: https://news.ycombinator.com/item?id=48614868 Points: 1 # Comments: 0

I got tired of typing my phone's ADB pairing info, but also don't usually have Android Studio open to display a QR code, so I Clauded up an all-bash (with qrencode installed) command to do it straight from the CLI. > brew install kristjan/tap/adbqr All that's needed is a join-network style string written through qrencode for the phone to scan: > WIFI:T:ADB;S: ;P: ;; For sake of pleasantness, if you're in iTerm2 or a terminal that supports Kitty, it'll print crisp imagery. Otherwise it falls back

<h1> Build Rails, Not Trains: A Framework for AI Infrastructure in the Global South </h1> <p>There's a question I ask before building anything:</p> <p><em>"What is missing?"</em></p> <p>Not: "How do I compete with what already exists?"</p> <p>The answer to the second question leads you toward incremental improvement.<br> The answer to the first question leads you toward infrastructure.</p> <h2> The Rail vs. The Train </h2> <p>TCP/IP didn't control the internet. It enabled it.<br> Railroads didn'

<p>I built a multilingual fact-checker using XLM-RoBERTa fine-tuned on the FEVER dataset. The first version hit F1 0.655. Not bad, but it kept misfiring on obvious real-world claims. Earth being the third planet from the Sun returned FALSE at 76% confidence. Something was fundamentally wrong.<br> A commenter identified the issue immediately: I was training the model on claims alone, with no evidence. FEVER is not a claim classification task. It's a Natural Language Inference task — the model is

<p>AI coding assistants are fast. They're also surprisingly consistent at making the same class of structural mistakes.</p> <p>After scanning hundreds of AI-generated files, I kept seeing the same patterns:<br> </p> <div class="highlight js-code-highlight"> <pre class="highlight python"><code><span class="c1"># Pattern 1: MISSING_WRITE # AI generates a save function that never actually saves </span><span class="k">def</span> <span class="nf">save_user</span><span class="p">(</span><span class="n

<h2> Why Python is still one of the best choices for AI </h2> <p>Python is popular in AI because it has a strong ecosystem, simple syntax, and great support for data processing, APIs, automation, and machine learning.</p> <p>For AI applications, Python works especially well for:</p> <ul> <li>Building backend AI services</li> <li>Connecting to LLM APIs</li> <li>Processing documents and text</li> <li>Creating automation workflows</li> <li>Building RAG and chatbot systems</li> <li>Integrating AI in

Article URL: https://xrpentest.com Comments URL: https://news.ycombinator.com/item?id=48614768 Points: 1 # Comments: 0

Article URL: https://theaidigest.org/village/blog/how-the-ai-village-works Comments URL: https://news.ycombinator.com/item?id=48614722 Points: 2 # Comments: 0

<p>When a CI gate flags an AI-generated PR, the important question is not only "what did it flag?"</p> <p>It is also:</p> <p>"Could someone else come back later and re-derive why this finding fired?"</p> <p>That is the reason I added evidence snapshots to Agent Gate v0.2.1.</p> <h2> What Agent Gate is </h2> <p>Agent Gate is a GitHub Action for AI-generated pull requests.</p> <p>It does not review code with an LLM. It checks deterministic merge evidence in CI:</p> <ul> <li>PR scope escapes</li> <

Article URL: https://vinibrasil.com/when-i-reject-ai-code-even-if-it-works/ Comments URL: https://news.ycombinator.com/item?id=48614631 Points: 54 # Comments: 25

The interview prep platform built for AI engineers. Practice real problems on Multi-Agent Systems, RAG, Vector Databases, and production AI architectures Comments URL: https://news.ycombinator.com/item?id=48614611 Points: 1 # Comments: 0

I've been using Ansible for IT automation for a while, and the process of writing imperative playbooks in something that looks closer to English I felt was a good fit to design MCP tooling around. I specifically wanted to have a process of running Ansible modules directly as tools from an LLM and record it so my exploratory sessions can become runnable playbooks. The ansible-doc command already emits the schema for each module, so tool calling is nice and structured, and it feels nice to have th

Article URL: https://www.bbc.com/news/articles/c0rygp005wjo Comments URL: https://news.ycombinator.com/item?id=48614390 Points: 49 # Comments: 47

Article URL: https://www.anthropic.com/research/project-fetch-phase-two Comments URL: https://news.ycombinator.com/item?id=48614311 Points: 41 # Comments: 14

Researchers later discovered what they could do. ScienceAlert stories are written, fact-checked, and edited by humans, never generated by AI. Don't miss a story, subscribe here.

<h2 id="introduction">Introduction</h2> <p>In <a href="https://engineering.grab.com/part-1-palana-for-autonomous-ai-agents">Part 1</a>, we introduced <strong>Palana</strong>, Grab’s Kubernetes-native secure execution platform for autonomous AI agents. We discussed the underlying need for isolated environments and covered its core design principles: treating isolation as the unit of trust, keeping credentials out of agent hands, and mediating all network access. In this second part, we’ll dive un

I've been vibe-coding tools to automate chunks of my consulting work, fell down a rabbit hole, and started building actual products. Suddenly I'm in a world of unknown-unknowns and known-unknowns. One of the bigger things to solve was understanding code the LLM generated that I didn't fully grasp. What does it touch? What reads and writes where? Is the auth path where I think it is? So I built codeflowmap. Point it at a repo and it maps the dependency and call graph, then surfaces the read / wri

Article URL: https://asiaai.fyi/east-asias-ai-capital-surge-homegrown-models-challenge-west-amid-mineral-tensions/ Comments URL: https://news.ycombinator.com/item?id=48614218 Points: 3 # Comments: 0

Hi I have been working on persistent memory. I wanted to see whether a brain like graph-based memory system could be used and more importantly how much we could save on llm tokens. FERNme uses fuzzy edge with Hebbian co-occurrence rule to create memory tags. I think FERNme could become a great personal memory of people that can be used by agents to give more personalized answers. The code is placed in: https://github.com/mirkofr/FERNme I invite developers to to check and test it and give honest

Article URL: https://video.intellios.ai Comments URL: https://news.ycombinator.com/item?id=48614087 Points: 2 # Comments: 0

Other than terminal bench which doesnt quite map to my experience, what are some other benchmarks to see how different models do in different harnesses? Comments URL: https://news.ycombinator.com/item?id=48614029 Points: 3 # Comments: 0

An AI company is sending free cleaners door-to-door in a bid to train the robots it hopes one day will replace them.

An AI company is sending free cleaners door-to-door in a bid to train the robots it hopes one day will replace them.

If we are witnessing a profound shift in the online space, how will it reshape our relationship with it? Technology and AI editor Zoe Kleinman writes.

I know frontier labs keep their flagship sizes top secret, but I'm curious what the current engineering consensus is. Comments URL: https://news.ycombinator.com/item?id=48613944 Points: 2 # Comments: 0

Cisco Foundation AI has open-sourced FAPO (Fully Automated Prompt Optimization), a Claude Code-driven system that autonomously optimizes multi-step LLM pipelines from baseline prompts to target accuracy. FAPO evaluates a chain, attributes failures at the step level, proposes variants across prompt, parameter, and chain-structure levels, and validates each through an independent reviewer. In Cisco's evaluation, it beat GEPA on 15 of 18 model-benchmark comparisons. Here's how the optimization loop

Article URL: https://github.com/BetaBots-LLC/callimachus Comments URL: https://news.ycombinator.com/item?id=48613813 Points: 3 # Comments: 2

Article URL: https://www.youtube.com/watch?v=B2ASCTomtIA Comments URL: https://news.ycombinator.com/item?id=48613703 Points: 3 # Comments: 0

Article URL: https://funnybench.lol Comments URL: https://news.ycombinator.com/item?id=48613679 Points: 4 # Comments: 2

Don't blink. ScienceAlert stories are written, fact-checked, and edited by humans, never generated by AI. Don't miss a story, subscribe here.

Disclaimer: This was made with LLMs. I made this tool to help understand large business processes that can be modelled as a single state machine. The core loop of this is to enable to walk stakeholders through discussing each step, adding comments, and reiterating with an LLM of their choice to generate the TLA+ syntax on the left. Users can click through the green state nodes to see how things work visually. You can see some sample state machines in the dropdown in the top left. The power would

Multi Agent Continuous Context Harness - MACCHA solves the problem that every AI coding session starts from zero. It combines a file-based 7-tier context architecture with a working memory engine (Memanto) that features vector embeddings, confidence decay, and semantic conflict detection — so Antigravity, OpenCode, and Claude Code all share the same persistent, self-improving brain. No 24/7 daemon needed. Comments URL: https://news.ycombinator.com/item?id=48613604 Points: 4 # Comments: 1