标签：#ai

Google released Gemini 3.6 Flash, 3.5 Flash-Lite, and Flash Cyber, but the continued absence of Gemini 3.5 Pro raises fresh questions about its AI strategy.

OpenAI launches the ChatGPT for Small Businesses program, helping entrepreneurs build AI skills, automate work, and grow with ChatGPT Work.

Twitch is giving parents more control over how their children are using the streaming platform, including the ability to block them from broadcasting entirely. Parental controls are now available that allow guardians to link Twitch accounts with their 13- to 17-year-old children, providing account management features and a weekly email summarizing their teen's activity, including […]

There are new 3.6 and 3.5 models today, but Google is already training Gemini 4.

A federal judge has signed off on Anthropic's $1.5 billion class action settlement with authors who accused the company of training its AI models on copyrighted books, as reported earlier by Reuters. In an order on Monday, Judge Araceli Martínez-Olguín writes that the settlement will provide "meaningful relief," offering authors around $3,000 for each book […]

Canvases turn AI into interactive workspaces where you can visualize information, explore workflows, and take action across complex tasks. The post How to build interactive experiences with canvases appeared first on The GitHub Blog .

Open-source LLMs stopped being the budget option in 2026. Kimi K3 sits level with Claude Opus 4.8 on the Artificial Analysis Intelligence Index (its hosted API is live; the weights themselves are expected by July 27), GLM-5.2 held the top open-model spot before it, and the field behind them is deep enough that the hard part is choosing. This ranking covers the nine best open-weight models right now — on license, context window, hardware reality, and the per-token price you actually pay. Every one of them is available through LLM Gateway with one key, at each provider's published rate, so you can A/B any two of them by changing one word in a request. 1. Kimi K3 — the open frontier Moonshot AI · 2.8T params · 1M context · $3.00 / $15.00 per M The largest open-weight model ever announced — with one caveat: the weights are not downloadable yet. Moonshot expects to release them by July 27, 2026, and the license is still unannounced; the hosted API has been live since July 16. Ranks 4th of 189 models on the Artificial Analysis Intelligence Index — tied with Claude Opus 4.8 and GPT-5.5 — and took first place in Arena's blind Frontend Code testing. Always-on reasoning, vision, tools, and output configurable up to 1M tokens. The open model to beat, priced accordingly. Full breakdown here . Best for: teams that want closed-frontier quality with open-weight freedom. 2. GLM-5.2 — the value flagship Z.ai · 744B params · 1M context · $1.40 / $4.40 per M MIT-licensed, weights on Hugging Face, and the top-ranked open model until K3 arrived. A real 1M-token context, strong agentic-coding results, and built-in web search support — with output at under a third of K3's rate and input at about half. Also the largest model on this list that fits a single 8-GPU node (or one 512 GB Mac Studio) at INT4. Best for: the best capability-per-dollar in the open field. 3. DeepSeek V4 Pro — frontier scale at commodity prices DeepSeek · 1.6T params (49B active) · 1M context · $0.435 / $0.87 per M MI

Kimi K3 took first place in Arena's Frontend Code evaluation the week it launched, and it holds a 1M-token context — but Moonshot doesn't ship a coding agent, and your coding agent doesn't ship Kimi K3. Claude Code is locked to Anthropic's API by default, Cursor to its own backend, Cline to whatever key you hand it. LLM Gateway bridges that gap. It speaks both the Anthropic and OpenAI API formats, so the tools you already use can run Kimi K3 — or any of 200+ models — with a base-URL change. Here is the exact setup for each tool. Kimi K3 in Claude Code Claude Code talks to any endpoint that speaks Anthropic's /v1/messages format, which LLM Gateway does natively. Three environment variables: export ANTHROPIC_BASE_URL = https://api.llmgateway.io export ANTHROPIC_AUTH_TOKEN = $LLM_GATEWAY_API_KEY export ANTHROPIC_MODEL = kimi-k3 claude That's the whole migration. Every request now routes through LLM Gateway to Kimi K3, and every request shows up in your dashboard with its exact cost, token counts, and cache-hit rate. One refinement worth adding: Claude Code uses a second, smaller model for routine background work, and you can point it at something cheap — or free: export ANTHROPIC_SMALL_FAST_MODEL = glm-4.7-flash-free That puts K3 on the hard reasoning and a $0 model on the housekeeping. Kimi K3 in Cursor Cursor routes its chat / plan panel (Cmd/Ctrl + L) through a custom OpenAI-compatible endpoint. Setup: Open Cursor Settings → Models Add your LLM Gateway key under OpenAI API Key Enable Override OpenAI Base URL and set it to https://api.llmgateway.io/v1 Add kimi-k3 as a custom model and select it Be aware of the boundary: Cursor's Composer, inline edit (Cmd/Ctrl + K), and autocomplete are locked to Cursor's own backend and will not route through any external endpoint. Plan and chat with K3's full 1M context in Cursor; if you want K3 driving the actual agent loop, use Claude Code or Cline instead. Kimi K3 in Cline Cline is the straightforward one — it's built to bring y

Kimi K3 is the first open-weight model that makes the Kimi K3 vs Claude Opus 4.8 question worth asking seriously. On the Artificial Analysis Intelligence Index, K3 ranks fourth of 189 models — level with Opus 4.8 and GPT-5.5, behind only Claude Fable 5 and GPT-5.6 Sol. It also costs 40% less per token, and its weights are expected to be released by July 27. The honest answer is not "K3 wins" or "Opus wins" — it depends on what you're optimizing for. Here are the numbers, then the verdict. Benchmarks: Kimi K3 vs Claude Opus 4.8 Benchmark Kimi K3 Claude Opus 4.8 Artificial Analysis Intelligence Index 57 (4th/189) statistical tie GPQA Diamond 93.5% 93.6% Terminal-Bench 2.1 88.3%* 74.6%* SWE-bench Verified not published 88.6% Arena Frontend Code 1st (1,679) ranked below *Reported on different harnesses (Opus 4.8's score uses the Terminus-2 harness), so treat the Terminal-Bench gap as directional, not exact. Three takeaways. On graduate-level reasoning (GPQA Diamond) the models are statistically tied. On agentic terminal work, K3's published number is well ahead, with the harness caveat above. And in Arena's blind Frontend Code testing, developers ranked K3 first outright — ahead of every model, including Anthropic's. Where Opus 4.8 keeps an edge: SWE-bench Verified at 88.6% is a published, battle-tested result K3 has no counterpart for yet, and Anthropic's models remain the default target for agent harnesses — Claude Code, MCP tooling, and most agentic scaffolds are tuned against Claude first. K3 is three days old; its ecosystem is not. Pricing: 40% cheaper across the board Per-token rates through LLM Gateway (each provider's published pricing): Per million tokens Kimi K3 Claude Opus 4.8 Input $3.00 $5.00 Cached input $0.30 $0.50 Output $15.00 $25.00 Both have a 1M-token context window. K3's output limit defaults to 131K tokens but is configurable up to the full 1M in a single response; Opus 4.8 caps output at 128K. Concrete math: a coding-agent workload of 100M input a

Moonshot AI released Kimi K3 on July 16, and the benchmarks put an open-weight model next to the best closed ones for the first time. The catch is access. K3 sits on Moonshot's platform, GLM-5.2 on Z.ai's, DeepSeek V4 Pro on DeepSeek's, MiniMax M3 on MiniMax's — four accounts, four billing relationships, four API dashboards, all before you have written a line of code. LLM Gateway routes every one of them through a single OpenAI-compatible endpoint. One key, one bill, and a switch between Kimi K3 and any of 200+ models is a one-word change to your request. What is Kimi K3? Kimi K3 is Moonshot AI's flagship model for long-horizon coding and agentic work. At 2.8 trillion parameters — a mixture-of-experts design that activates 16 of its 896 experts per token — it is the largest open-weight model announced to date. Moonshot has committed to publishing the full weights by July 27, 2026. The specs that matter in practice: 1M-token context window (1,048,576 tokens), with output configurable up to the same 1M — enough to hold a large repository plus its docs in a single request Always-on reasoning — K3 thinks before every answer; there is no non-thinking mode Vision, tool calls, and JSON output supported out of the box Prompt caching at a 90% discount on repeated input Early results back up the size. K3 ranks fourth of 189 models on the Artificial Analysis Intelligence Index — level with Claude Opus 4.8 and GPT-5.5 — and took first place in Arena's Frontend Code evaluation in blind developer testing. It posted 93.5% on GPQA Diamond and 88.3% on Terminal-Bench 2.1, the strongest open-weight results published on both at release. Kimi K3 pricing Through LLM Gateway you pay Moonshot's published per-token rates: Tokens Price per million Input $3.00 Cached input $0.30 Output $15.00 The cached-input rate is the number to watch. Coding agents re-send the same system prompt, file context, and conversation history on every step, so in a long agent session most of your input tokens are

How Normal Software Engineers Actually Use AI in Their Daily Work Let's cut through the hype. You're not building the next AGI. You're a working software engineer with deadlines, legacy code, and a backlog that never shrinks. So how do you actually use AI tools in your day-to-day work? After surveying hundreds of developers and reflecting on real-world usage patterns, here's what normal software engineers are doing with AI—no Silicon Valley theatrics required. The Mundane But Invaluable: Code Completion and Boilerplate The most common use case is the least sexy: letting AI handle repetitive code. GitHub Copilot, Cursor, and similar tools excel at generating boilerplate that you'd otherwise copy-paste from Stack Overflow or previous projects. Real example: Writing CRUD endpoints in Express/TypeScript: typescript // Type this comment and let AI complete: // Create a REST endpoint for user registration with email validation app.post('/api/users/register', async (req: Request, res: Response) => { try { const { email, password, name } = req.body; // Email validation const emailRegex = /^[^\s@]+@[^\s@]+\.[^\s@]+$/; if (!emailRegex.test(email)) { return res.status(400).json({ error: 'Invalid email format' }); } // Check if user exists const existingUser = await User.findOne({ email }); if (existingUser) { return res.status(409).json({ error: 'User already exists' }); } // Hash password and create user const hashedPassword = await bcrypt.hash(password, 10); const user = await User.create({ email, password: hashedPassword, name }); res.status(201).json({ userId: user.id, email: user.email }); } catch (error) { res.status(500).json({ error: 'Internal server error' }); } }); Did AI write perfect code? No. But it gave you scaffolding to refine, saving 10-15 minutes of typing. That's the real win. The Game-Changer: Explaining Legacy Code and Obscure APIs Every developer inherits someone else's mess. AI tools shine when deciphering undocumented code or unfamiliar libraries. Pract

The first Lego X-Files set costs $200 and will be available in August.

I've spent my career helping people build software. This is a series of letters about what happens when the tools for building change faster than the principles behind building. Each one is a reminder that while the technology changes quickly, the questions that matter often stay the same. -- Dear past Jenna, The thing that drew you to tech in the first place, that it's always changing, is the thing that will keep you here. Tools change (sometimes for the better and sometimes not) almost weekly. People who never consider themselves technical, much less a developer, will build apps in an afternoon with this new programming language called English. Products will go from idea to deployed before you finish your first cup of coffee (you have a toddler now, so you rarely get the full cup before it goes cold anyways). "I don't know how to code" or "I'm not technical" is no longer a barrier to building. And that's exciting, given you've focused nearly your entire career helping others build software. But we can't confuse the ability to build with the wisdom to build. One of the most valuable habits you've developed over the last two decades is asking a simple question: "Should we build it?" For most of your career, "Can we build it?" was a hard question. Time, budget, complexity, maybe the tech wasn't there yet. But those constraints were usually temporary. With enough people, time, and money, just about anything is possible. But the real questions were always: Should we build this? Is this the best use of our time? Does this solve a problem our customers actually need solved, or does it create new problems? What are we choosing not to build? Those questions haven't changed, but the environment around them has. Today, almost anyone can build software. Between tools like Lovable, Bolt, Replit, Claude Code, Codex, and whatever's next, the barrier to building software is lower than it's ever been. Now the question "Can we build it?" is too easy to answer. It's almost always ye

Treasury Secretary Scott Bessent said the U.S. could sanction Chinese open AI models over alleged IP theft, expanding the Trump administration's campaign to slow China's AI advances.

Spotify's DJ-inspired tools are loads of fun, but probably won't land you any bar mitzvah gigs.

Google is launching Gemini 3.6 Flash alongside a new security model dedicated to quickly finding and patching security vulnerabilities. In a blog post on Tuesday, Google describes Gemini 3.5 Flash Cyber as a "cost-efficient and highly capable alternative" to larger, more expensive AI systems, such as the one offered by Anthropic's Mythos. The cybersecurity model […]

Learn more about Paramount's planned acquisition of Warner Bros. Discovery — a historic Hollywood megadeal valued at $111 billion — as it continues to develop.

It's important to be aware of disc rot, but there's no need to be afraid.

Deezer said more than 90,000 AI-generated tracks were uploaded daily on the platform in June.

I first slipped on Halliday's original smart glasses at CES 2025. I was not a fan. The glasses had a tiny, movable display window embedded into the frame that was incredibly finicky to see, and my 30-minute demo left me with achy eyeballs. It was an interesting concept with terrible execution, especially compared to the […]