Most people think managing a long-running coding agent means either sitting at your desk watching logs, or walking away and hoping it didn't get stuck! Google's newly launched Antigravity Remote Control argues you shouldn't have to choose either. It's browser-first, no dedicated app required, and lets you connect to an active session running on any of your machines, laptop, desktop, or a cloud box, from your phone. You're not babysitting the agent anymore. You're checking in on it the way you'd check a notification, reviewing the plan, approving what needs approval, and moving on with your day. In this video, Julian breaks down what Antigravity really is and you need to watch it! Follow @neil_xbt for more!

Kept hearing everyone point to Higgsfield for faceless viral videos, $15 to $129 a month depending on tier. Checked Sabrina's actual cost breakdown instead of just taking the recommendation at face value. The $15 comparison is the part that actually changed my mind. The skill driving it is free, built from @robonuggets' guide originally, then extended with a spend cap and a quality check before any video gets animated. Genuinely didn't expect the actual model behind Higgsfield's pricing to just be pay-as-you-go access marked up into a subscription. The article below gives you the free tier!

Until I build my first gaming PC, I will be using this to understand more about each component. I created The Rig as a way to build custom desktop PCs and get actual pricing. This is less about putting parts together and more about building an actual PC that will run without issues. Built this with @threejs and Claude in only a few hours. Use it for yourself here and let me know what to add to it: 3d-rig.vercel.app/

Most people who try this fail in the exact same way: they land one client, delivery eats the week, outreach quietly stops happening, and three months later they've got one client, no pipeline, and the wrong conclusion that "this doesn't work." > Why it happens: five hours can't run acquisition and delivery at once, only ten can > The fix, alternate in phases instead of blending: build, then acquire, then deliver, then add client two > The deliverable rule: one bounded artifact only; "content support" invites scope creep that eats your entire margin > Three services that actually fit: a weekly research briefing, four content pieces/month, or one maintained automation > The two-hour first step: define the niche in one sentence, then time yourself building one real sample Watch for the "and" in your one-sentence pitch, if it's there, you've got two deliverables, not one. Full plan below.

ethereum:0xfaba6f8e4a5e8ab82f62fe7c39859fa577269be3 Stocks crossed $1 billion in TVL in May 2026, the fastest any tokenized asset class has reached that threshold, faster than stablecoins (roughly three years) or tokenized Treasuries (roughly two years). > Ondo Stocks currently shows 440+ tokenized assets and holds over 70% market share among tokenized equity issuers. > Tokenized stocks provide eligible non-U.S. users exposure to U.S. stocks and ETFs with extended, near-24/7 onchain availability > JPMorgan/Kinexys, Mastercard, Ripple, and Ondo already completed a near-real-time cross-border redemption involving tokenized U.S. Treasuries > Tokenized stocks and ETFs are already usable as margin collateral on Ondo Perps, cutting required capital nearly in half for market makers > The progression so far: stablecoins first, then Treasuries, now stocks and ETFs, with those assets becoming DeFi collateral as the next stage already underway The broader RWA story isn't that individual assets went onchain. It's that brokerage-level infrastructure is being assembled onchain, piece by piece. Follow @neil_xbt for more breakdowns of where RWA is actually heading, beyond the headline numbers.

A dev just open-sourced the exact category of product xAI charges monthly for, an autonomous AI bot platform, except this one you fully own! > Every bot gets persistent memory and its own dedicated computer, not a one-off chat session > Bots can spawn child bots or short-lived subagents to divide up work automatically > Ships with a real live Linux desktop the agent can see and operate, not just a terminal > Runs on Docker, a remote sandbox, or your own desktop, your choice > Model-agnostic: point it at OpenRouter, OpenAI, GitHub Copilot, or Grok itself This isn't a wrapper around one company's API. It's the entire agent-with-a-computer concept, decoupled from the subscription. Follow @neil_xbt for the frameworks quietly replacing the products that inspired them. Repo: github.com/elie222/rakazo

CUDA Agent, developed by ByteDance Seed and Tsinghua University's Institute for AI Industry Research, is the first known RL-trained model to surpass advanced proprietary models like Claude Opus 4.5 and Gemini 3 Pro on high-performance CUDA kernel generation! > Delivered 100%, 100%, and 92% faster execution than PyTorch's torch.compile on KernelBench Level-1, Level-2, and Level-3 splits > Beat Claude Opus 4.5 and Gemini 3 Pro by roughly 40% on the hardest Level-3 benchmark > Built on large-scale agentic reinforcement learning rather than static prompting or simple multi-turn bug fixing > Trained in a skill-augmented environment with automated hardware verification, profiling, and synthetic data pipelines providing reliable reward signals > Used a synthesised training dataset of 6,000 filtered operator tasks (CUDA-Agent-Ops-6K), with contamination controls to exclude anything too similar to the actual benchmark > Training data, the expert-designed SKILL.md, and the full agent environment were released publicly The researchers frame this as moving LLMs from passive code generators toward active, hardware-aware system optimisers. Source: cuda-agent.github.io/ Follow @neil_xbt for more breakdowns of real AI research results, not just the headline claim.

A team out of Tsinghua just proved the trick to making AI good at CAD isn't a smarter model; it's four smaller ones working in sequence! > One agent turns your text description into structured requirements > A second agent designs the actual geometric blueprint > A third agent writes the real Python code that builds it > A fourth agent tests the output and repairs it automatically > Routine operations like extrudes and fillets skip the AI entirely and run on hand-coded logic > Each stage can run on a different model, so you mix providers by cost or capability The result: over 100x fewer tokens, roughly 13x lower cost, and a higher pass rate than the single-agent approach it was benchmarked against. This isn't a chatbot that outputs code. It's an assembly line, and every station is auditable. Repo: github.com/Pan-Chera/Multi-A… Follow @neil_xbt for the frameworks quietly changing what AI can build.

2 days to build. 4 weeks to trust. Those are two different clocks, and every company OS that quietly bleeds money is one where somebody treated them as the same clock. > 9 elements: vocabulary, truth, gates, memory, a real agent behind a fence, all buildable in a weekend > 5 checks that prove the weekend build actually works before you touch week one > 4 rollout stages, each one gated behind a graduation criterion the last stage earns > 5 walls your own agent has to survive you attacking before it ever spends real money > 1 sentence that explains the whole thing: autonomy is evidence you produced, not confidence you feel Build fast. Adopt slow. Full breakdown below.

Ranking every crypto narrative by how real its market cap actually is (MCap/FDV): 1. Privacy — 1.00 (led by $ZEC, $XMR) 2. CEX Tokens — 0.84 3. Bridges — 0.80 4. Zero Knowledge — 0.76 5. Oracles — 0.73 Down to Tap-to-Earn at 0.21. High ratio = priced in already. Low ratio = the real test comes at unlock. Smart money believes in Privacy and you should too.

He built Empire Atlas, a 3D interactive explorer of 8 historical empires using three.js and Kimi K3. Explore homes, daily life, interiors, maps, all properly researched. I've said it before: history sticks when you can actually walk through it instead of reading about it in black-and-white textbook photos. Here's the part that actually surprised me: > This was near one shot vibe coded with kimi k3, unlike a previous 3D anatomy app where i had to manually iterate on performance > The moment I handed over 3D assets, prompt, and design, kimi generated its own 11 step engineering plan and executed it > Its first move was optimising assets on its own, taking 500MB of 3D models down to 17.8MB using mesh simplification, Draco compression, and 1024px WebP textures > It also batch-generated 56 historical images across all 8 empires and converted them to WebP, landing around 10MB total Only downside: the whole build took more than 5 hours. And to be honest, that is not even a downside compared to the hours needed without AI. Repo: github.com/thebuggeddev/empi… Follow @neil_xbt for more vibe-coded builds that went past the demo stage.

Most people assume convincing VFX in the browser means textures, sprite sheets, and pre-baked assets, because that's how games have always cheated realism into existence cheaply! This open-source Elemental Sandbox, built in Three.js, argues otherwise. Every crystal, bolt, and burn is procedural, generated entirely through hand-written GLSL and GPU-simulated particles, with world position resolved directly in the vertex shader. 938 live parameters stay editable even mid-cast, mid-pause. Nothing on screen was authored in an art tool. It was authored in math. Repo: github.com/achrefelouafi/Lin…

This is the failure mode nobody talks about: You can build a company OS that spends, ships, and hires in two days flat, and have zero evidence about whether its judgment matches yours, because evidence takes weeks and building took a weekend. In this article, here is what you will find: > Why "it runs" and "it can be left alone" are two completely different claims > The four-week rollout: Observe → Gate → Connect → Operate, in that exact order > The one week where being wrong is free, and why skipping it is the real risk > The specific test for whether your gate actually rejects anything or just looks like it does > Why week four literally cannot happen on day one, no matter how good the build looks Bookmark it. The rules are compressed at the bottom of the guide below.

A motion design studio charges $2,000+ to produce one cinematic product video! This open-source AI skill does it for free, and it already has 4,400 stars! Point it at your product and it storyboards, animates, and sound-designs a complete promo, no editor needed. 152 shot recipe cards distilled from the actual motion language of ClickUp, Notion, Figma, Slack, and Perplexity's own product videos. 209 motion previews you can browse before generating anything. Ships a full validated 36-second 1080p template ready to swap in your own screenshots. Built entirely by an AI coding agent, using the same workflow it teaches you. Most builders are still paying freelancers for a 30-second promo. The ones who found this are shipping cinematic video from a text prompt. Repo: github.com/Vincentwei1021/vi… Follow @neil_xbt for AI engineering intelligence that finds tools like this before they're everywhere.

An Anthropic engineer, in a live 10-minute build from a blank terminal, put the internal shift directly: "90% of our engineers were using self-improving loops. Now everyone has shifted to building agentic Graphs." She added: "No more prompting." What normally costs $1,000 in an agentic engineering course is demonstrated here for free, straight from the source. Bookmark so you do not lose it! Follow @neil_xbt for more!

"Autonomy is evidence you produced, not confidence you feel." That one line explains why most company-OS builds quietly cost their owner money, not because they were built badly, but because build time and trust time got treated as the same clock. > 2 days to build the whole system, 4 weeks to actually trust it > Nine elements: from typed vocabulary to a real agent behind a fence > The gate rule: spend, hire, publish, terminate all queue for a yes, no exceptions > The fence test: five walls you're supposed to attack yourself before week four > The graduation criteria for every single stage, and what to do when one fails Save this before you turn on autonomy on a Monday because it exists.

Most people think a one-line prompt gets you a one-line result, something literal and minimal! Actually, Claude Opus 5 and GPT 5.6 Sol were given exactly one sentence: build a living medieval town you can watch from above as the day goes by, and neither model was told to add citizens. Both did anyway, because a town without people isn't actually a town. Opus 5 went further, naming its own town Marrowmere, counting 155 roofs and 150 souls, running a real clock, and letting you follow one specific citizen through their entire day. The gap between what you asked for and what got built is where the actual capability shows up. Follow @neil_xbt for more on what AI models do when nobody specifies the details.

A coding agent will generate any file you can describe in a paragraph in under a minute, for cents, and nine elements of a full company operating system add up to a single weekend of prompting and checking. The build is no longer the hard part, and treating it as though it still is, by turning on the autonomous elements the moment the build finishes, is the specific mistake that produces an expensive lesson rather than a working system. This article breaks down the actual steps to create a fully functioning system. Bookmark it so you can read it till Week 4!