标签：#m

In Crazy Taxi World Tour, The Offspring is back, and fish is on the menu.

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 […]

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 […]

Bending Spoons say its app caters to a user base of over 500 million monthly active users.

Two years ago, when Microsoft first revealed that it was bringing four Xbox-exclusive games to the PS5 and Nintendo Switch, it made the announcement far more complicated than necessary. That's not likely to improve anytime soon. In fact, things now seem more confusing than ever as the company tries to appease both fans and the […]

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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

NSO Group is still targeting WhatsApp users, according to Meta.

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

When we build something complex—whether it’s a skyscraper, a gourmet meal, or a piece of software—we usually start with a plan. In software development, however, it’s easy to skip that step. We often jump straight into implementation, focusing on how to write the code instead of the intent behind it. Over time, this leads to rework, confusion, and systems that don't quite match our original goals. Spec-Driven Development (SDD) is an approach that shifts the focus back to the plan. Instead of starting with code, you start with a Specification : a clear, structured description of what the software should do. You then use an AI coding agent as a high-speed collaborator to help turn that specification into working code. 🔍 What is a “Spec”? A Specification (or “Spec”) is a written contract between your intention and the final product. It isn't a 50-page manual; it's a living document that defines: What the system should do. How it should behave in different scenarios. Which constraints and rules it must follow. From Prompts to Specifications There is a massive difference between a vague prompt and a structured spec. Loose prompts often lead to inconsistent results and "hallucinations," whereas clear specifications give the AI a much better target to hit. Bad Prompt: > “Build me a login system.” Good Spec: A good spec provides the clarity an AI (or a human) needs to succeed. You don’t need a 10-page document to benefit from specs; you need clarity, not length. 🛠️ Example Spec: Login Endpoint Overview Allow users to log in using email and password. Endpoint POST /api/login Request { "email" : "user@example.com" , "password" : "string" } Behavior Success: If email and password are correct → return a token and user info. Invalid Credentials: If credentials don't match → return INVALID_CREDENTIALS . Invalid Input: If fields are empty or the email format is wrong → return INVALID_INPUT . Rules Passwords must be stored hashed (e.g., bcrypt). Token expires in 24 hours. Security:

"# The AI Cost Crisis: How Startups Can Survive the Tokenpocalypse\n\n## Introduction\n\nThe artificial intelligence boom has brought unprecedented innovation, but it has also ushered in a era of spiraling costs. Training state-of-the-art models now requires millions of dollars in compute resources, while simultaneously, the cryptocurrency token market shows signs of a potential collapse—a \"Tokenpocalypse.\" For AI startups, this dual crisis presents an existential threat: how to sustain innovation when both traditional funding avenues and speculative token economies are under pressure? This post explores practical strategies for AI startups to navigate this landscape, focusing on cost optimization, alternative funding, and strategic pivots that can turn crisis into opportunity.\n\n## Understanding the Cost Explosion\n\n### The Compute Crunch\n\nModern AI models, particularly large language models (LLMs) and multimodal systems, demand vast computational resources. Training a single cutting-edge model can consume exaflops of processing power, translating to cloud bills that easily exceed $10 million for a single training run. For startups without deep-pocketed backers, these costs are prohibitive.\n\n### The Token Market Volatility\n\nParallel to the AI boom, the cryptocurrency space experienced explosive growth through token launches—initial coin offerings (ICOs), decentralized finance (DeFi) tokens, and utility tokens for AI-driven projects. However, regulatory crackdowns, market saturation, and declining investor sentiment have led to a sharp downturn. Many tokens have lost significant value, and launching new tokens has become increasingly difficult, removing a once-viable funding path for AI startups.\n\n## Strategies for Survival\n\n### 1. Embrace Model Efficiency\n\nInstead of chasing ever-larger models, startups can focus on efficiency techniques that deliver comparable performance at a fraction of the cost:\n\n- Model Distillation : Train smaller \"student\

Used 5 different programming language for this course. GitHub repository link: https://github.com/mohammadijoo/Control_and_Robotics_Tutorials submitted by /u/abolfazl1363 [link] [留言]

I've started a series of short blog posts about hot path optimizations. This first one covers a counterintuitive optimization: replacing integer division ( IDIVQ ) with floating-point division ( DIVSD ). submitted by /u/watman12 [link] [留言]

Lenovo's IdeaPad Slim 5x just might be the best laptop you can buy for $850. It's hard to find any major flaws, which is basically unheard of in a laptop at this price.

Google says Gemma 4 12B is "designed to bring agentic, multimodal intelligence directly to your laptop", further noting that the new model can be combined with Google AI Edge to "build and experiment locally, on everyday machines". This integration allows for a wide range of capabilities, from autonomous data processing to generating visual insights and even building webpages or executing tools. By Sergio De Simone