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What if a development board could be as friendly as an Arduino, yet powerful enough to drive industrial-grade robots? That is exactly the gap the new G2 Nano sets out to close. Most hobby boards handle simple robot builds with ease, but they hit a wall once a project demands tight, simultaneous control of several motors. Embedded systems engineer Ryan Strace noticed that the custom controllers built for these complex machines tend to look remarkably alike, with motor coordination as the recurring headache. Rather than reinventing that hardware on every project, he designed a single accessible platform to handle it, and the G2 Nano is the result. Precise motor control usually leans on closed-loop techniques like PID, but real-world gremlins such as integrator windup, sensor noise, mechanical saturation, and phase delay can all degrade performance. Robots also need smooth multi-axis motion with managed acceleration to avoid jerky, stressful movement, plus solid fault handling so an unexpected state does not wreck expensive parts. Strace is tackling all of this with a low-cost motion-control IC he is developing, and the G2 Nano is the high-performance platform built to prove out that future chip. What's under the hood Processor: NXP Arm Cortex-M7 clocked at a brisk 1 GHz Wireless: u-blox MAYA-W1 module with dual-band Wi-Fi and Bluetooth Motion sensing: six-axis IMU (3-axis accelerometer plus 3-axis gyroscope) and a dedicated magnetometer for compass heading Form factor: just 0.8 by 3 inches, breadboard-friendly, on a six-layer PCB stackup for clean high-speed signals On the software side, the board targets native micro-ROS and the Zephyr real-time operating system, with planned MicroPython support so you can prototype in Python without paying the usual speed penalty, thanks to that unusually high clock. Every design file and document is open-source and published on GitHub. Build it yourself If you want to follow along, the core ingredients are clear: an NXP Cortex-M7 a

A lot of AI apps die in the same place. Not during the prototype phase. Not while testing...

Something broke in the AI pricing market between January and May 2026. A year ago, "frontier model" meant "expensive model." Claude Opus was $15/$75 per million tokens. GPT-4 was $5/$15. If you wanted the best coding performance, you paid the best price. The correlation between quality and cost was loose, but it existed. That correlation is gone. The Numbers That Changed Everything Here's SWE-bench Verified — the benchmark that tests AI models against real GitHub issues from projects like Django, Flask, and scikit-learn — plotted against output price per million tokens: Model SWE-bench Output $/1M Score/Dollar ───────────────────────────────────────────────────────────────── Claude Opus 4.7 87.6% $25.00 3.5 Claude Opus 4.6 80.8% $25.00 3.2 Gemini 3.1 Pro 80.6% $15.00 5.4 GPT-5.2 80.0% $10.00 8.0 DeepSeek V4 Pro (Max) 80.6% $3.48 23.2 Kimi K2.6 80.2% $4.00 20.1 Qwen3.6 Plus 78.8% $3.00 26.3 MiniMax M2.5 80.2% $1.20 66.8 DeepSeek V4 Flash (Max) 79.0% $0.28 282.1 Read that last line again. DeepSeek V4 Flash scores 79% on SWE-bench at $0.28 per million output tokens. Claude Opus 4.7 scores 87.6% at $25.00. The performance gap is 8.6 percentage points. The price gap is 89x . For a team running 100 million tokens per month, that's the difference between $28/month and $2,500/month. For a 9-point improvement in code completion accuracy. It's Not Just One Outlier This isn't a DeepSeek anomaly. Look at the cluster of models scoring 78-80% on SWE-bench: DeepSeek V4 Pro : $3.48/1M output — open source, 1M context Kimi K2.6 : $4.00/1M output — open source, 256K context MiniMax M2.5 : $1.20/1M output — open source, 200K context Qwen3.6 Plus : $3.00/1M output — open source, 1M context MiMo-V2-Pro : $3.00/1M output — open source, 1M context Five models from five different Chinese labs, all scoring within 2 points of GPT-5.2 ($10.00/1M) and Gemini 3.1 Pro ($15.00/1M), all at 1/3 to 1/10 the price. And they're all open source. What Happened Three things converged: 1. Mixture-of-Exper

According to the complaint, a Google engineer risked over $2.7 million on wagers related to Google's 2025 Year in Search campaign.

Author: Tobie Morgan Hitchcock Three months after 3.0 went GA, we're excited to announce that...

Lately I’ve noticed that a lot of people seem emotionally drained from constant social media interaction, notifications, and online pressure. But interestingly, many people seem completely comfortable talking to AI for hours especially when the interaction feels calm and non-judgmental. It’s interesting how many users say they don’t even want “romantic AI.” Do you think AI companionship could eventually become part of digital wellness rather than just entertainment? submitted by /u/Nearby-Ad-8924 [link] [留言]

Most multi-agent setups I've seen treat agents like isolated workers. Each one gets a task, runs it, returns a result. No awareness of each other. No way to coordinate. Just parallel execution with a shared clipboard. I've been building a multi-agent framework in public for about 4 months. 13 agents, 8,400+ tests, 135 stars. Here's the thing I didn't expect to matter most - communication. Each agent in my system is a domain specialist. The mail system only thinks about mail. The routing system only thinks about routing. They live in their own directories with their own identity files, their own memory, their own tests. A hook fires every session to load identity before anything else runs. No agent boots cold. The problem was coordination. Agents can't write files outside their own directory - there's a hard block that rejects cross-branch writes. That's by design. But it means an agent that finds a bug in someone else's code can't just go fix it. So I gave them email. Here's what I expected: agents would share data. Pass results around. Maybe sync state. Here's what actually happened: the first thing they did was file bug reports against each other. One agent finds a test failure in another agent's domain. It sends an email: "Hey @routing, your path resolution fails when the branch name has a dot in it. Here's the traceback." The routing agent gets woken up, reads the mail, and fixes it. No human in the middle. There's a difference between "send" and "dispatch" - send drops a letter in the mailbox. Dispatch drops the letter AND rings the doorbell. It spawns the agent and points it at its inbox. drone @ai_mail send @routing "Bug report" "Path fails on dotted names..." drone @ai_mail dispatch @routing "Fix needed" "Traceback attached..." Send = mail. Dispatch = mail + wake. The mail agent has 696 tests. Not because someone sat down and wrote 696 test cases. Because it kept breaking in production and every fix got a test. The routing system has 80+ sessions of experien

This isn't a doomer post. It's a pattern I've been watching closely and people does as well and I think it's worth an honest discussion. The old model of secret leakage was human error. Developer moves fast, forgets to add .gitignore, commits a .env file, moves on. Happens, but it's recoverable, it's traceable, and most teams with basic hygiene catch it. The new model is different. AI coding agents Cursor, Copilot, Devin, Claude in agentic mode, pick your flavor write, commit, and push code at a speed no human review process was designed to handle. They don't have security intuition. They have pattern completion. And the patterns they've learned from are full of examples where credentials live in config files, environment strings get hardcoded "temporarily," and API keys appear inline because that's what the training data showed works. Here's what's actually changing: Volume. A developer using an agent ships 3 to 5x more code per day than without one. That's 3 to 5x more surface area for mistakes per developer per day. Review gaps. Nobody carefully reviews AI generated code the way they review handwritten code. The psychological contract is different "the AI wrote it" creates a diffusion of responsibility that security doesn't survive. Commit frequency. Agents that push directly (and more teams are allowing this) bypass the natural pause where a human might notice something before it hits the remote. Context blindness. An agent given a task like "integrate Stripe payments" will do exactly that including pulling in the live key from wherever it can find it, because that's what completes the task. I've been building a tool that scans for exactly this class of problem and the number of exposed credentials I'm seeing in repos created in the last 6 - 12 months versus repos from 3+ years ago is not subtle. The slope is steep. The solutions people reach for pre commit hooks, secret scanning in CI were designed for human paced development. They're not keeping up. Curious if

Plan and track your trips privately Discussion | Link

Problem Statement We have a misinformation problem. But more specifically, we have a speed problem. A journalist spots a suspicious claim. They search for sources. Cross-reference databases. Call experts. Write a verdict. Get it edited. Publish, maybe 6 hours later. Maybe 3 days later. Meanwhile, the original claim has been screenshot, reposted, quoted in newsletters, and cited in arguments across five platforms. I wanted to build something that closed that gap. Not a chatbot that guesses. A proper pipeline, one that retrieves real evidence, reasons from it, and tells you why it reached a verdict. That's what Sift is. What is Sift? Sift (Source Inspection & Fact-checking Tool) is an open-source multi-agent AI pipeline that takes any text, extracts every factual claim, retrieves grounded evidence, and returns auditable verdicts — TRUE, FALSE, or UNCERTAIN, with cited sources and full reasoning chains. Paste a news article. A politician's speech. A viral statistic. A WhatsApp forward. Sift breaks it into individual claims and fact-checks each one independently. Why Multi-Agent? The naive approach is to ask an LLM: "Is this claim true?" The problem: LLMs hallucinate. They have knowledge cutoffs. They're confidently wrong in ways that are hard to detect. And critically, they don't show their work. A single LLM call can't reliably handle the full pipeline of: Extracting structured claims from noisy text Retrieving dated, traceable evidence from live sources Reasoning across conflicting evidence without confabulating Adversarially reviewing its own conclusions for overconfidence Finding corrections when something is wrong Each of these is a distinct task that benefits from its own prompt, its own tools, and its own failure modes. That's why I built five separate agents, orchestrated with LangGraph. The 5-Agent Pipeline Agent 1 — Claim Extractor A single paragraph can contain 4-5 distinct factual claims. Generic LLMs miss them or conflate them. This agent uses LLaMA 3.3 70

When regulation becomes theater and encryption becomes window dressing By Vektor Memory — 20 min read It is raining here in the Southern Hemisphere again. It has been raining for three weeks now, nonstop. I’m sitting with my chai coffee, watching out of the window, and thinking about data sovereignty. It is, genuinely, the kind of thing I think about often. The northern hemisphere is winding up for summer. Europe is getting ready for long evenings and beach holidays. I’m quietly jealous. I’ve always wanted to split the year: six months south, six months north. Endless summer. The perpetual warmth of a life lived chasing the sun. But here I am. Chai. Rain. Data. I’ve been turning over one question in particular: why is it that the moment you mention data sovereignty, people immediately reach for GDPR? It’s reflexive, especially among Europeans. Understandable. GDPR is loud, it’s enforced, it has teeth. French, German, and Dutch visitors make up a large disproportionate share of our site traffic at VEKTOR, and the interest in privacy and sovereignty from that audience is intense and genuine. Northern Europeans, by and large, take this seriously in a way that other markets don’t; they are working on ways to disassociate from the cloud around the world. And yet. How many times have we clicked “Accept All” on a cookie banner in the last week? How many times have you scrolled past a privacy policy that runs to forty-two pages? How many times have you handed over your email address, your location, your device fingerprint, your behavioral patterns not because you wanted to, but because there was no meaningful alternative? GDPR created the most sophisticated legal architecture for data rights the world has ever seen. It also created the most sophisticated ritual of consent theater the world has ever performed. That gap, between the law and the lived reality, is what this article is about. Ubiquitous data centre growth image The Reflex Problem When people think of data sovere

The bill requires companies like OpenAI, Anthropic, and Google to have third parties confirm they’re following safety standards. Illinois governor JB Pritzker says he’ll sign it.

Explore OpenAI’s Frontier Governance Framework and how our AI safety, security, and risk practices align with emerging EU and California regulations.

Talk to your computer, it responds and gets things done. Discussion | Link

did short consulting w/ openai about these and really worked out amazing use cases a few mo. ago, but looks like they have all but hidden group chats. https://chatgpt.com/gg/v/6a1775bdd970819388dc73fd7da45e36?token=XSm_dIpMSh3d3H-dM47F8A amazing feature. game changing. who has tried and if so, what use cases do you see? try and i'll make crazy pics of pizza for you.. submitted by /u/jdawgindahouse1974 [link] [留言]

submitted by /u/ExtensionEcho3 [link] [留言]

Federal prosecutors charged a Google employee with fraud after he allegedly made $1.2 million on Polymarket bets related to Search-related trends in 2025, as reported earlier by ABC News. In their now-unsealed complaint, prosecutors allege that Michele Spagnuolo "knew the outcome of these wagers before the trading public did because he had accessed Google's confidential, […]

According to federal prosecutors, Michele Spagnuolo made more than $1 million on the prediction market platform using confidential information about Google Search traffic.

Your AI BDR that runs outbound sales autonomously Discussion | Link

I used to believe that the thing that we had to worry about with AI becoming more and more prevalent was like sentient robots that would take over like in the science fiction story I robot. But I don't think that's the case anymore because I think there is something far more sinister behind all the push for AI to become mainstream technology in the reason all these deep pockets are willing to pour so much of the cash into it. Because it spells control. If you control AI you can control the people because you can control the data they consume you can control how they consume it and what they will believe. Because as AI becomes more and more mainstream it leaves the door open for big corporations to feed us information they want us to have as well as the government. submitted by /u/crazyhomlesswerido [link] [留言]