Gunther

@misat0x Great article Choosing a market, not just a language, is the part most creators miss

@misat0x this feels like a huge unlock for smaller creators going global

MrBeast reportedly spends tens of millions every year making his videos feel native in other languages. This guy just showed the small-creator version of that strategy: take one video dub it into 40+ languages keep the original voice sync the lips The demo is impressive. But the interesting part is not AI dubbing. It is what happens after the dub. Most creators treat a finished TikTok as one post. The smarter move is to treat it as a source asset. One useful English tutorial can become a version for Mexico and Colombia. Then Brazil. Then another market where the same problem exists, but the content still feels imported. Not by throwing subtitles on it. By rebuilding the hook, slang, examples, captions, and on-screen text until a viewer feels: this was made for me. That is the opportunity. The new creator advantage is not making more videos. It is giving the good ones more than one chance to win. More audience. More feedback. More ways to make money. I mapped out the full workflow: which market to test first, how to localize the script, dub it with ElevenLabs, fix the visuals, and turn the winners into a repeatable system. The tools just made global distribution cheap. Making content that actually feels local is still hard. Full article below 👇

@KeisukeIshikawa free official courses with certs is actually a huge win for ai learners

ANTHROPIC JUST DROPPED 13 FREE COURSES ON CLAUDE, WITH CERTIFICATES, AND CHARGED $0 FOR ALL OF IT. no subscription, no credit card, no upsell. just an email and you're in. the company that charges for the model is handing you the manual to it for free. → 13 courses: Claude 101, Agent Skills, the Claude API, Model Context Protocol → a 27-minute prompting tutorial from Anthropic's own engineers → a prompt-engineering masterclass in Jupyter notebooks → a full Claude Code guide by Cal Rueb → an official prompt library for Claude Code, sorted by task and role → every course ends with a certificate you can put on LinkedIn people are paying for $500 courses to learn this. anthropic just made the definitive version free. follow and bookmark. this is the cheapest closing your AI skill gap will ever be.

@pomiderix live artifacts feels slept on, this is actually a wild use case

Anthropic just dropped Live Artifacts. Barely anyone is talking about it. A guy built 5 dashboards that run his entire business in 10 minutes total. Each one pulls live data from Gmail, Calendar, Stripe, and any app he uses. His 5 dashboards: Morning command center — emails, priorities, week ahead Stripe revenue tracker — live payments and outstanding invoices Meeting prep — who's joining + 3 talking points before every call Competitor tracker — scrapes trending videos in his niche daily Skills dashboard — every Claude skill in one panel 2 minutes each. Zero code. Zero plugins. Just prompts. He didn't automate his workflow. He turned Claude into a CEO cockpit. Save this. The full 17 hidden Claude features are in the article below ↓

@Abobsterina 200k stars is wild, the gbrain setup sounds genuinely useful

THE HERMES ECOSYSTEM JUST BLEW PAST 200K STARS, AND THE CEO OF Y COMBINATOR IS PERSONALLY WRITING CODE FOR IT. The agent itself is only half the story now. The layer being built on top is where it gets interesting. Five repos worth knowing. 1.GBrain — ~23K stars github.com/garrytan/gbrain Garry Tan, President and CEO of Y Combinator, built this to run his own agents and open-sourced it. A markdown-first memory layer that wires its own knowledge graph — typed entities like people, companies, and investments extracted with zero LLM calls. His production brain: 146,646 pages, 24,585 people, 5,339 companies, 66 cron jobs. A nightly “dream cycle” enriches pages and fixes citations while he sleeps. His words: “I wake up smarter than when I went to bed.” Hermes memory handles sessions. GBrain handles institutional knowledge. 2.hermes-workspace github.com/outsourc-e/her… Nous Hackathon 2026 winner. Full web GUI: chat, embedded terminal, memory browser, skills manager, live sub-agent streaming. Docker one-click. If the CLI keeps you from going deeper, start here. 3.mission-control — 5K+ stars github.com/builderz-labs/… Fleet management for multi-agent setups. One dashboard across all your profiles: dispatch tasks, track cost per agent per day, coordinate workflows. Self-hosted, SQLite, zero external dependencies. Makes sense the day you run three or more profiles and lose track of what they cost. 4.SkillClaw github.com/AMAP-ML/SkillC… Auto-evolves your skill library from real session data. Deduplicates overlapping skills, improves them from usage patterns, and ships safety flows: skillclaw doctor checks health, skillclaw restore recovers from a bad evolution. 5.agenttrace github.com/luoyuctl/agent… Local-first TUI for post-run audits. Token spike detection, tool failure tracking, retry loop detection, session-to-session diffs, CI gates that fail builds on unhealthy runs. Nothing uploads anywhere. Run it the morning after your cron jobs, before the mistakes compound. The detail most people miss: this is the exact shape npm took around Node. The runtime stopped being the product, and the ecosystem became the moat. When the CEO of YC ships a memory layer for an open-source agent instead of funding one, that tells you where he thinks the value landed. Most people are still comparing chatbots. The infrastructure war moved to the layer above.

@maloymediika this is such a clean way to make notes actually useful

EVERYONE TAKES NOTES WRONG. 1 AI TRICK MAKES THEM 10X SMARTER. Everyone chases a bigger model. Wrong question. The bottleneck is retrieval, not intelligence. A single router file inside Obsidian tells Claude Code where to look: wiki folder here, podcast notes there, daily logs below. It walks the map, pulls the right notes, answers with citations back to the source. Here is where it stings. People pay $20 a month for ChatGPT memory that forgets last week. A plain markdown folder plus one routing doc gives you a second brain that cites every claim and compounds every day. The audience sees a note-taking app. The operator sees a queryable memory layer.

@caesar_aii this is the kind of chaos i respect for local ai

FOUR RTX 5090S TURN AN OPEN FRAME INTO A PRIVATE INFERENCE ROOM The video starts with the ugly part. Loose GeForce RTX cards. An AX1200i. A ROG Thor 1200W. Another 1000W unit. Then someone flips the power strip and the whole bench lights up. That is the real local AI PC story. Not a cute desktop under a monitor. A loud workstation built for one reason: keep models running in the room. No cloud queue. No API limit. No client data leaving the machine. No subscription stack deciding when work stops. This is not for “one chatbot.” It is for local Qwen, DeepSeek, Llama-style models, embeddings, batch transcription, code agents on private repos, and Open WebUI / Ollama / llama.cpp sitting on a LAN box everyone can hit. Ownership changes the math. Cloud GPUs are cleaner for bursts. Local cards win when the workload is daily, private, repetitive, and expensive to pause. The tradeoff is brutal: power, heat, risers, drivers, PCIe lanes, RAM, storage, and noise. Four flagship GPUs do not become a magic business machine because they glow. But this is where AI hardware is moving. Fewer “AI apps.” More rooms with owned compute doing boring work all day.

@BLAZT_AI the vision pro part is what got me, portable compute with a giant virtual screen is a cool combo

THIS TINY POWERBANK KEPT A MAC MINI M4 RUNNING FOR 3 HOURS AND 40 MINUTES STRAIGHT. Idle draw? Just 1.5W. Light browsing and work? Only 3-5W. That's the whole secret — Apple Silicon's efficiency is so extreme that a compact powerbank can basically turn a Mac Mini into a portable workstation. Screen setup starts with an iPad as a wireless display — full touch and Apple Pencil control built in. Then he switches it up: Apple Vision Pro as the display instead, giving him a massive virtual screen with zero physical monitor needed. No wall outlet. No bulky battery pack. Just one small powerbank pushing nearly four hours of real, uninterrupted compute. This is what untethered computing actually looks like now.

@Neuron_404 the differences are getting subtle now but motion and atmosphere still give each model away

ONE PROMPT, THREE AI MODELS - AND YOU SEE WHO REALLY PULLS OFF CINEMA. kling 2.5 turbo, sora 2 and veo 3 got the same cinematic prompts - a whale in the clouds, a magic battle - and each generated its own version. this is a head-to-head of the three strongest ai video generators on identical scenes, frame for frame. > three ai video models, one prompt > kling 2.5 turbo vs sora 2 vs veo 3 > you see the difference frame for frame the gap between ai video models is shrinking - the choice is down to nuances now. this is the new ai video race - three models, one prompt. watch and judge for yourself. follow me so you don't miss out on trends in the world of ai.

@misat0x agree

@GuntherWrite i’d rather watch a messy demo than a perfect ad

@Psalteric shock too.

@GuntherWrite 500$ pet video is crazy

@caspr_exe the scale alone is crazy 8 years and that many people really shows in the details

$1.5 billion. 8 years. Over 1,000 people. And not one pixel of this was generated by AI. Take-Two's CEO called an AI-made GTA "laughable" and kept generative AI out of the entire game. Every building, every street, every wave was placed by a human. GTA 5 grossed roughly $10 billion, the most profitable entertainment product in history. GTA 6 will likely beat it, with zero AI in the product. In a world where anyone can generate average in seconds, the most valuable thing on earth is still what humans made by hand.

@0xzynex makes me wonder if calm content is the real edge now

HE FILMED AN iMAC UNBOXING WITH NO TALKING AND PEOPLE WATCHED IT TWICE For years an unboxing meant noise. A face in the corner, a voice reading specs off a box, ten minutes of "hit that subscribe." So the actual thing got lost. The peel of the film. The weight of the stand. The quiet click when a cable finally seats. Nobody was listening for it. Then he did it differently. No voiceover. Just the box, his hands, and every small sound the iMac makes coming out of the packaging. From the outside it looks like the simplest video on the feed. A tape pull. A lid lifting. A screen waking up against a clean wall. The first reaction is always the same. Why is this so satisfying to watch? Then you notice you've stopped scrolling. The tearing tape, the soft thud of the base on the desk, the moment everything finally sits in its place. It slows your whole body down. The part that lands later is the trade. He said almost nothing and held more attention than the loudest tech channel on the app. No hype. Nothing to sell. And here is what nobody tells you. The iMac was never just a computer to unbox. It was a few minutes of calm people didn't know they needed. He turned setting up a desk into something you rewatch. Most creators are still shouting over the exact sounds he let you hear. The best unboxing is the one that says nothing at all.

@silentguyy66 free credits are the best growth hack in ai right now

THE AI COLD WAR HAS A FREE TIER everyone debates claude vs chatgpt vs deepseek meanwhile chinese labs give away more free compute than any western provider not because they're generous developer mindshare is the new oil i've been running both ecosystems for months quality gap is shrinking fast access gap is already zero the real moat isn't the model it's who got you hooked on free credits first

@Psalteric cloudflare moving fast here, more room to actually build things

OpenAI, xAI, and Meta dropped frontier-level AI models on the same day — and Cloudflare responded by making AI agents cheaper to run. But the real story isn’t another benchmark leaderboard. It’s how quickly frontier intelligence is turning into infrastructure. Here’s what happened: – OpenAI released the GPT-5.6 series publicly, pushing coding performance forward again – xAI launched a new model designed to compete at the frontier — with benchmarks placing it alongside the leading labs – Meta released Spark 1.1, an agentic coding model with serious capabilities at a fraction of the usual price – Then Cloudflare reset its weekly rate limits for all users — giving developers more room to actually build with these models The takeaway: frontier AI is no longer just getting smarter. It’s getting cheaper, more accessible, and easier to deploy at scale. Three labs shipped. Infrastructure providers reacted. Developers got more leverage overnight. The AI race just shifted from “Who has the best model?” to “Who can turn these models into working products fastest?”

@Zyron5m this is such a clean way to frame randomness in crypto

A man who flipped burgers for ten years now spends his life proving where randomness comes from, and he put all of cryptography in one sentence: "There are no secrets without randomness." That's Avi Wigderson. He has a free lecture that asks one question: where does real randomness actually come from? The answer is: almost nowhere you can trust. Randomness looks like it is everywhere. The weather, the stock market, the noise inside any physical thing you can measure. Buried inside is a catch almost nobody feels. You can gather a mountain of unpredictable bits and still fail to squeeze out one fair coin, if those bits lean on each other the wrong way. Your gut reads more entropy as more safety, and a long messy sample as proof. Wrong both times. The flaw is invisible to human intuition, which is exactly why cryptographers hand the job to the math. None of it is hidden. Von Neumann showed how to clean a biased coin back in the 1950s. Santha and Vazirani proved the hard wall in the mid 1980s: for the worst kind of dependent source, no function on Earth beats reading the very first bit. The lecture is free. Here is the trap: you feel every bit you collect, every extra sample, as more security. What you cannot feel is whether those bits are truly independent. And independence is the only thing that pays. One correlation you never see can drain the entropy out of a whole sequence, and almost everyone trusts the pile long before they check it. The randomness is free to gather. The discipline to prove it is really random before you stake a secret on it, that part you still have to bring yourself.

@0x_mura wild how far a tight workflow can beat giant budgets now

🚨Amazon reportedly spent over $465,000,000 producing a single season of The Rings of Power. a 22-year-old spent $50 last month tracking which formats were already winning and cleared $11,900 running the pattern herself. > the swipe feed tracks 40 channels for anything breaking out: daily > Claude reshapes the winning format around a new subject: minutes > CapCut builds the vertical, voice, and captions: automatic > Make drops it everywhere and pulls the score back: scheduled $50/month in tools. $11,900 last month. amazon spent nearly half a billion on one season. she spent $50 finding the shape that already worked. the swipe feed setup sits below.

@expemillyweb3 markdown plus agents feels like the most underrated ai workflow right now

Karpathy just built an agent stack that gets smarter every night he sleeps. started as a throwaway gist. blew up in a week. the reframe kills every "AI memory" startup: your notes are the codebase. claude is the programmer. you stop asking questions - you build a system that asks them for you. 3 loops run it: /> ingest: drop a paper. subagent splits it into atomic notes, wires each one to everything you already know. /> query: ask anything. it answers from your vault, in your words, citing your own pages - never hallucinating. /> prune: 3:14am every night, a second agent walks all 1,847 notes. kills stale claims. flags contradictions. no vector DB, no embeddings, no $200/mo memory app - just markdown and a loop that never sleeps. your $500 "second brain" course covered maybe 17% of this. I stopped taking notes this week because claude does it now. full setup below:

@kyroxxxq this is the kind of automation that actually feels useful

A 19-YEAR-OLD IN FRANCE TURNED A DEAD $599 MAC MINI INTO SOMETHING THAT OUTPERFORMS AN $800/MONTH EMPLOYEE. The detail most people miss: if Claude can’t find at least 2 real connections to existing notes, it is not allowed to call the task done. It goes back and tries again. Every article he bookmarked and never opened gets the same treatment, every night at 11 PM. Summarized. Weak arguments flagged. Fluff deleted. At 6:30 AM, before he is even awake, Claude Haiku picks one old note, writes a real question about it, and sends it to his phone. Cost of that entire morning routine: under $0.01. Monthly API bill: under $15. A virtual assistant doing this manually would run $800 a month. He tested every step by hand first, in a normal chat, 3 times each, before automating anything. You do not get faster at typing. You stop being the one pushing the process through every step.

@danglar_ this is exactly why simpler model choices feel underrated sometimes

Derek’s coworker replied to the thread three days later. “You said $340. Where’d it go.” Model credits, mostly. Cursor’s buffet GPT-5, Opus, Gemini, switching mid-task hunting for whichever one wasn’t hallucinating that hour. Half the spend was indecision, not compute. Claude Code has two models and a slash command. That’s the whole menu. “Feels like a downgrade,” his coworker said. “It’s not picking for you. That’s the point.” He’d watched Cursor’s agent mode chew through a feature build overnight background VM, its own branch, a PR waiting by morning. Genuinely useful. He kept that. What he dropped was the auto-mode roulette. Paying for four models to babysit one task. Now the split is simple. Buffet for the big autonomous builds where he wants a PR he didn’t watch get made. Terminal for anything he needs to trust completely, right now, no diff to review because there’s no time to review it. Two tools. Two different kinds of trust. Neither one earned $340 worth of confusion.