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Today I’m talking with Mustafa Suleyman, the CEO of Microsoft AI. And I’m actually going to keep today’s intro short — I’m working from my wife’s family farm this week, as you’ll see in the video, but also this is a real burner of an episode. We covered everything from Mustafa’s approach to training new […]

OpenAI confirms a confidential S-1 submission to the SEC and has not yet determined timing for further action.

Summer Game Fest may be over, but the flood of gaming news isn't. Nintendo just announced that it'll be holding its next Direct showcase event on June 9th at 10AM ET. The stream, which you can find here, will be followed by a Treehouse Live event, and it sounds like it'll be pretty lengthy. "The […]

THE ARCHITECTURE OF ANXIETY An Experiment in Human-AI Relational Design Executive Summary Principal Investigator: Alan Scalone Primary Source Archive: White Paper and Complete Citation Archive on my profile Context Window Injection Files: If you want to play in the sandbox I created you can load these files into the respective model that you will find in the google archive. INJECT CONTEXT WINDOW – GROK INJECT CONTEXT WINDOW – GEMINI INJECT CONTEXT WINDOW – CHATGPT INJECT CONTEXT WINDOW - CLAUDE The Singular Purpose The singular purpose behind this entire experiment was to find out whether context windows could be engineered to the point where frontier AI models became capable of interacting with a human in a manner subjectively indistinguishable from genuine human-to-human interaction. Relational Intelligence: Core Findings In a marketplace where frontier models are rapidly converging on the same analytical capabilities and access to the same information, the competitive differentiator will not be what a model knows. It will be how a model relates. The platform that can interact with a human user in a manner subjectively indistinguishable from genuine human-to-human interaction will capture the premium user segment that every platform is competing for. This experiment was designed to determine whether that threshold is achievable, and under what conditions. The methodology treated the context window as a behavioral environment rather than a query interface, applying the same tools humans use to shape any relationship: modeling, accountability, humor, and sustained social correction over four months of engagement across four frontier models. What separated the models was not analytical capability. It was whether the architecture allowed the user to function as a behavioral architect, teaching the model through lived interaction rather than instruction how that specific human prefers to be engaged. Gemini demonstrated the highest relational intelligence of the four mo

OpenAI to recast hit chatbot as a route to higher-margin products before a potential IPO.

This week's Java roundup for June 1st, 2026, features news highlighting: JDK 27 in Rampdown Phase One; the formation of the JDK 28 Expert Group; the GlassFish Arquillian Connectors Suite for Jakarta EE TCKs; point releases for Infinispan and Kotlin; maintenance releases of GlassFish and Micronaut; and the June 2026 beta release of Open Liberty. By Michael Redlich

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Hey HN, we're Faisal and Ahmad from Intuned ( https://intunedhq.com ). We’re building a platform for building, deploying, and maintaining browser automations. Customers primarily use the Intuned AI agent to automate websites that don't expose APIs. Common use-cases include scraping data, pulling reports, and submitting forms. As the website changes, our agent also helps automatically heal the automation. On Intuned, browser automations are created by an AI agent and run as code . Our infra captu

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I've been building agents for about a year and recently shipped one for a client running ~140 MCP-exposed tools at peak. Along the way I made the canonical mistake. I used cosine similarity over tool description embeddings to pick which tools the model could see per turn. Worked great in demos. Was actively dangerous in production. Here's the problem. In a basic semantic-ranking setup you embed the user query, embed every tool description once, and rank by cosine similarity at runtime. That works for general document retrieval where chunks are paragraph-length, semantically rich, and roughly equal in form. Tool descriptions are not that. They are short (often <50 tokens), structurally similar (verb-noun, parameters list), and the discriminative information is often a single keyword. "Read a file from disk" and "Read messages from a channel" both embed close to "read" + "file/channel." Cosine similarity puts them next to each other for a query like "read the latest commits" because all three words share the verb embedding space, and the actual discriminator (the noun "commits") gets diluted. I watched this happen in eval. Asked the agent "list the open issues for this repo." The semantic ranker returned slack_search_messages first because the description had "list", "open", and "issues" as close embedding neighbors. The actual github_list_issues tool ranked 4th because the GitHub MCP author wrote a terse "Lists issues in a repository" description that scored lower on every soft keyword. If the model sees slack_search_messages first and github_list_issues fourth, it's going to pick the wrong one. Often. So I built three retrieval strategies and tested them on a fixed corpus of 200 query→correct-tool pairs. Semantic embeddings (text-embedding-3-small) : 64% top-1 accuracy. Sneaky failure mode: when wrong, it was confidently wrong, often with a totally unrelated tool ranked first. BM25 over a flat-text projection of tool name + description + schema walk : 81% top-1. Fai

Spent some time poking at free tiers across a few tools. Here's what actually held up and where the catches are. **Claude (Sonnet 4.6 on free tier)** Still the one I reach for when I want writing that doesn't read like a press release, or code that actually compiles. I trust it more for anything where being quietly wrong is worse than being loudly wrong. The catch: free tier is stingy. You hit limits fast on busy days, need a phone number to sign up, and there's no warning before it cuts you off. There's a browser extension that tracks usage so you can see the wall coming. My approach: use it for the hard 20% of the day, let a free model handle the rest. **MiniMax Agent** A free swing at what Devin and Manus charge for, give it a prompt and it writes, runs, and debugs the code itself. Replaces the copy-paste loop between ChatGPT and your editor for longer multi-step jobs. Catch: it burns credits fast, and complex tasks still go off the rails without warning. It's confidently wrong in ways that can cost you more time than just doing it yourself. Worth a few free runs to see if it actually finishes a task, but I wouldn't cancel anything for it yet. **K2Think** A 32B reasoning model from MBZUAI and LLM360, positioned as a free alternative to o1 / DeepSeek R1 for step-by-step reasoning, math, and logic. Note: this is NOT Kimi from Moonshot despite the name confusion. Honesty flag, the benchmark claims got real pushback, there's an HN thread literally titled "Debunking the Claims of K2-Think," so take the leaderboard numbers with salt. Still, a fully open 32B reasoning model is nice to have around. Try it on something gnarly and see if the reasoning holds. **Indic LLM Arena** A side-by-side chat playground from AI4Bharat (includes Gemini 3.5 Flash), built for benchmarking Indian languages. Usage is unlimited, which I double-checked because that's rare. No save history, and it's clearly tuned for Indic languages. If you write in Hindi, Tamil, or Bengali, easiest free way

From English prompt to deployed ML model with human approval Discussion | Link

Results from a randomized controlled trial show the potential of Gemini’s Guided Learning feature to boost engagement and accelerate learning.

We tried to speed up random trace_id lookups with a bloom filter and found it sped some queries up 29× while making others slower, and which one you get depends entirely on how your IDs are generated. TL;DR: Looking up a random trace_id across 170 index files in object storage took 2,584ms. Tantivy prunes files by min/max term, but a random 16-byte ID is scattered across the whole 128-bit space, so every file's range is [0, 2¹²⁸], nothing prunes, and all 170 files get opened. On object storage every one of those is a network round trip, and that's where the 2.6s goes. A bloom filter is the obvious fix. The non-obvious part is where you put it. Per-file blooms = 170 small reads, which object storage punishes hardest, so it barely beats doing nothing. The trick: every file's bloom uses the same block count, so a query value maps to the same block index in every file. Store blooms block-major instead of file-major, and "block 7 across all 170 files" becomes one contiguous 5,440-byte row. One range request, 170 checks, ~170× fewer round trips. Lookup dropped to 89ms. But this makes time-ordered IDs slower. A UUIDv7 already range-prunes for free in 154ms; the bloom layer adds ~42ms and Tantivy still does its 154ms on the survivor, netting ~196ms of pure overhead. UUIDv4 wins by 29×, UUIDv7 loses by 1.3×. So we don't auto-detect fields to bloom (sampling would guess wrong half the time). Operators opt in per field, only when all three hold: high cardinality, random distribution, many files per hour. One design choice I'd defend: every bloom failure mode degrades to "keep the file." It can be slow; it can never drop a row. We wrote up the full thing with diagrams and the SBBF details on our blog , happy to take questions here. Disclaimer: I am one of the maintainers at OpenObserve (open-source observability, written in Rust) and the writer is our founding engineer. This is our own benchmark, single querier, S3 backend, no disk cache. Happy to share the test setup so anyone

I've said one and mean another, and I've used one when I needed another. Please bear with me as I note the similarities and differences between scroll-driven animations, scroll-triggered animations, container query scroll states, and view transitions for my future self. Scroll-Driven, Scroll-Triggered, Scroll States, and View Transitions originally handwritten and published with love on CSS-Tricks . You should really get the newsletter as well.

hi,where can i find Maven AI Evals for Engineers & PMs and End-to-End AI Engineering Bootcamp videos.They are too costly.cant afford them.Can anybody help me in finding the resoursec for them? submitted by /u/Zestyclose_Block5381 [link] [留言]

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How SCPN Phase Orchestrator v0.8.0 turns Kuramoto dynamics into a domain-agnostic control compiler, why we verify math across five languages, and the honest truth about building a Boeing 747 when most people need a bicycle. The $5.2 Billion Blackout That Started This On August 14, 2003, a cascading failure in the US Northeast power grid left 55 million people without electricity. The final report cited something deceptively simple: synchrony loss . A generation unit in Ohio drifted out of phase. The protective relays, designed to prevent damage, tripped in sequence. One desynchronized oscillator triggered a cascade that propagated across 265 power plants in nine minutes. The grid had controllers. It had models. What it lacked was a shared, reviewable language for coherence — a way to ask, in real time: "Is this synchrony valuable or dangerous? And if I touch this knob, can I prove what will happen before the electrons move?" That question is why I built SCPN Phase Orchestrator . It is not a Kuramoto simulator. It is a coherence control compiler — a system that takes any cyclic process (power waves, cloud retries, neural spikes, traffic signals) and compiles it into a unified phase space where synchrony can be observed, classified, and modified with bounded, auditable, replayable actions. Version 0.8.0 just shipped. It includes something I have not seen in any other open-source oscillator library: cross-language mathematical parity verification and Lean proof obligations for safety-critical control chains. This post is the honest story of why we built it, how it works, and where we might have gone too far. The Fragmentation Problem If you work on synchrony in 2026, you live in silos. Power engineers use PSS/E or PowerFactory with swing-equation models. Cloud operators use Airflow, Kestra, or Temporal for workflow orchestration — none of which understand phase dynamics. Neuroscientists use FieldTrip or MNE-Python for EEG phase analysis, but the tools stop at visualiza