标签：#AI

ABC is firing back at the Federal Communications Commission after the agency opened an investigation into The View's airtime of political candidates. In a letter to the FCC on Tuesday, ABC argues that the agency's actions pose a risk to editorial independence by targeting programs "perceived as unfriendly to the current administration," as reported earlier […]

Netflix is bringing 2- to 20-minute videos to its platform through new partnerships with digital publishers, including Rolling Stone and Variety.

The T70S can be eligible for racing events or built to be road-legal.

With this update, users can start a task from their desk, get status updates on their phone, and pick up the finished output later — even if their laptop is closed.

It's early, but the plan is to reduce dependency on Nvidia and Huawei.

As part of the mass layoffs hitting Xbox, Doom developer id Software has laid off around 50 percent of its staff, according to Game Developer. One source claimed to the publication that the cuts equate to more than 90 redundancies. Another source said that id's QA department was significantly impacted. The report was published the […]

Max subscribers get first access to Claude's new Cowork tool for your phone.

One-Command Deployment: Self-Host Your AI Wallet with Docker and GHCR Would you trust a third party with your AI agent's private keys? If that question makes you uncomfortable, you're already thinking about self-hosting your wallet infrastructure — and WAIaaS makes it genuinely practical with a single Docker command. This post walks through how to get a fully self-hosted Wallet-as-a-Service running on your own server, with your own keys, under your own rules. Why Self-Hosting Your AI Wallet Actually Matters The rise of autonomous AI agents changes the stakes around custody. When a human manages a wallet, they can pause, verify, and think before signing. An AI agent operates continuously, potentially making hundreds of transactions — so the infrastructure holding those keys becomes critically important. Hosted wallet services make a trade-off: you get convenience in exchange for trusting someone else's server, someone else's rate limits, and someone else's uptime SLA. For many teams building experimental agents, that's fine. But for anyone running production workloads, handling real funds, or operating in environments with strict data residency requirements, the calculus shifts. Self-hosting gives you: Full key custody — private keys never leave your infrastructure No rate limits imposed by a third party — your server, your throughput Auditability — WAIaaS is open-source, so you can read every line of code handling your keys Network control — bind to localhost, put it behind a VPN, restrict egress however you want WAIaaS is built specifically for this use case: a self-hosted, open-source Wallet-as-a-Service designed for AI agents, deployable in one command. The One-Command Start The Docker image is published to GitHub Container Registry (GHCR) at ghcr.io/minhoyoo-iotrust/waiaas:latest . The fastest path to a running instance is: git clone https://github.com/minhoyoo-iotrust/WAIaaS.git cd WAIaaS docker compose up -d That's it. The daemon starts on port 3100 , bound to

X is rolling out a new video editor and recorder for iOS with multilingual captions, green-screen effects, and other editing tools.

AI law startup Norm has raised a $120 million Series C round led by Khosla Ventures, valuing the startup at $1.2 billion.

Bidbus, which lets dealerships bid on used cars, has raised $15 million in a Series A round that was led by early-stage mobility fund Ibex Investors.

Solos announced a new version of its AirGo smart glasses, one that forgoes cameras for a sleeker design and an AI assistant that relies on voice interactions. Last year's AirGo A5 weighed 36 to 40 grams depending on the frame style, but the new AirGo A6 weigh around 19 grams. Part of the weight savings […]

Most of the "AI for observability" work I see right now hands the language model the judgment. I think that's backwards. Feed it the alert, feed it some metrics, ask it what's wrong, what should be done, and let it make the judgement call. Based on my experience working with language models, I decided that inverting the process provides better results. The short version: in my alerting pipeline, the set of allowable classifications is fixed in deterministic Python, and the model has to pick from it. The LLM's only job is to turn a structured verdict into an easily digestible sentence. It never decides whether something is bad, how bad it is, or what category of problem it is. It narrates within a decision space the code has already locked down. TL;DR: Instead of letting an LLM decide what's wrong with an alert, I let deterministic Python make every operational decision and restrict the model to explaining the result in plain English. The code classifies, validates, and aggregates; the LLM only narrates. That keeps the data consistent, prevents hallucinated classifications, and ensures the monitoring pipeline continues working even if the model fails. The problem I run a small managed monitoring service. Alertmanager fires, a webhook lands, and historically that webhook produced a line like HighMemoryUsage on host web-vm, severity warning, which is accurate, but not terribly helpful. The person reading it still has to know what HighMemoryUsage implies, whether this host always runs hot, and whether to care. I wanted plain-English context attached to the alerts without altering the alert delivery process. The obvious move was to throw the whole alert at an LLM and ask it to explain. I tried that in the first iteration of this experiment, expecting it to be somewhat accurate, but not entirely reliable, and it did not disappoint, the model was confidently inconsistent. The same alert, fired three times, produced three different "root cause" categories. One run called a

Nephew saw a YouTube ad. Someone was selling a "secret prompt" for ₹199, claiming ChatGPT and Claude can analyze the stock market and place trades with 90% accuracy — no technical analysis, no fundamentals, just paste this prompt. He brings it straight to Uncle. The Ad 👦 Nephew: Uncle, I saw an ad on YouTube. Some guy was saying, "Use ChatGPT and Claude AI for stock market analysis, take trades with 90% accuracy. You don't even need to know technical analysis or fundamentals — just use this prompt and you'll get all the results." Is that actually possible? 👨‍🦳 Uncle: (laughs) Ah, here we go. This is exactly how a lot of scams happen — and honestly, it's rarely because of some clever new invention. It's because of a lack of understanding, and people treating these models as a magic black box. 👦 Nephew: So are they scamming us? Or genuinely fooling themselves too? 👨‍🦳 Uncle: Not exactly a straightforward scam, and not exactly genuine either. Here's the honest split: they're maybe 30% correct, and 70% wrong. 👦 Nephew: What does that even mean? 👨‍🦳 Uncle: I'll accept this much — there genuinely are AI models out there that can do a solid job predicting stock trends or running fundamental analysis, because that kind of prediction is heavily mathematical, numerical work. But — and this is the important part — ChatGPT, Claude, and Gemini are not that kind of model. 👦 Nephew: Why not? It can literally write code. It can do math inside code. Why can't it just... do math for stock prediction too? I genuinely don't get it. 👨‍🦳 Uncle: Come, sit. This needs a proper, from-scratch conversation. We're going to dig all the way down to what these models actually are , and by the end, you'll understand exactly why ChatGPT, Claude, and Gemini are the wrong tool for this specific job — not a scam exactly, but sold by people who never actually opened the box themselves. Part 1: What Is an LLM, Really — In One Honest Sentence 👨‍🦳 Uncle: Before anything else, one sentence, and hold onto i

The Problem When building BotForge, our AI no-code chatbot platform, we needed a retrieval system that could handle messy, real-world user queries — typos, partial phrases, semantically similar-but-differently-worded questions. A naive vector search alone wasn't good enough. It's powerful but brittle to out-of-vocabulary terms and exact keyword lookups. The Solution: Four-Tier Parallel Retrieval We ran four retrieval strategies simultaneously using Promise.all\ , then merged results with a weighted scoring function. \ javascript const [semanticResults, textResults, regexResults, fuzzyResults] = await Promise.all([ semanticSearch(query, embeddings), // weight 1.8x mongoFullTextSearch(query), // weight 1.5x regexKeywordSearch(query), // weight 1.0x fuzzyPerWordMatch(query), // weight 0.6x ]) \ \ Tier 1: Semantic Search (1.8× weight) Using Gemini gemini-embedding-2\ to produce 3072-dimensional vectors , we compute cosine similarity against stored document embeddings. This catches meaning — "how do I reset my login?" matches "account recovery options" even with no shared words. Tier 2: MongoDB Full-Text Search (1.5× weight) A native MongoDB Atlas text index for fast, exact keyword hits. Great for technical terms, product names, and precise phrases. Tier 3: Regex Keyword Matching (1.0× weight) Each significant word in the query is compiled to a case-insensitive regex. Catches partial matches and hyphenated variants. Tier 4: Fuzzy Per-Word Matching (0.6× weight) Levenshtein distance matching per query word — handles typos and misspellings like "configuraton" → "configuration". Weighted Score Merging Each result carries a base score from its retrieval strategy. We deduplicate by chunk ID, sum scores across strategies, and sort descending: \ javascript function mergeResults(tiers, weights) { const scoreMap = new Map() tiers.forEach((results, i) => { results.forEach(({ id, score, chunk }) => { const weighted = score * weights[i] scoreMap.set(id, { chunk, total: (scoreMap.get

I hesitated to write this. Not because I don’t have an opinion about AI in software engineering, but...

The company just raised $7 million in seed funding, and is launching its app for iPhone and Android on Tuesday.

It's another bad week for the video game industry. Microsoft outlined a series of layoffs on Monday that Xbox CEO Asha Sharma described as "the most significant restructure in Xbox history." But buried in Sharma's memo was a curiously optimistic statement: "I want Xbox to be one of the few companies that entertains more than […]

Top robotics researchers and founders explain how robot autonomy is evolving.

Top robotics researchers and founders explain how robot autonomy is evolving.