sonicdr1p

@vikktorrrre The goalposts move so fast now. Fable 5 is the new bar and everyone else is reacting to it.

"just behind fable" is the new benchmark now big win for Anthropic and a massive L for OpenAI they're no longer the guy to beat so much for the release of GPT-5.6 Sol and Kimi K3 lol

@0x_mura Smart. Most people guess what might work. He just studies what already did on similar channels and copies the pattern.

🚨TikTok spends $2,000,000,000 a year on the algorithm that predicts what goes viral before you see it. a 19-year-old built his own version for $50 a month and made $11,900 from it last month. he tracks 40 channels in one Google Sheet, scores every outlier against the channel's own median, and feeds the top patterns to Claude. > Claude extracts the format and writes the shot list: 20 minutes > CapCut builds the vertical: 40 minutes > Make publishes to every platform and logs the result: automatic $50/month in tools. 4 hours to set up. runs every day since. most channels post first and hope. he checks what already won, then points it at a new subject. full breakdown of the scoring method below.

@melfoy_work This is the kind of release that actually matters. Once you can download the weights and control it yourself, the subscription model starts feeling outdated.

China handed the world’s most powerful open model to anyone with a download link. No API key. No subscription. No vendor who can take it back. Kimi K3. 2.8 trillion parameters. 1 million token context. Native vision. Beat Claude Fable 5 and GPT-5.6 Sol on independent coding benchmarks. $3 per million tokens. Cache drops it to $0.30. 18 months ago Moonshot AI looked finished. DeepSeek ate their market. The Kimi brand was a footnote in China’s AI race. July 27 they drop the full weights under MIT license. Download once. Fine-tune it on your domain. Run it air-gapped. No lab on earth can reprice it or deprecate it. Every founder who lost sleep over model deprecation notices just got a frontier-class fallback that answers to nobody. The frontier used to be a subscription. In 10 days it becomes a file.

@vovudebosh Sounds interesting. The research and verification part is usually what takes the most time with these tools. Curious how accurate it actually is in practice.

BREAKING 🚨: THIS IS THE FIRST AI SALES TOOL THAT DOES THE WHOLE JOB. NOT JUST DATA. NOT JUST ENRICHMENT. NOT JUST TEMPLATES. RESEARCH, VERIFICATION, AND A PERSONALIZED COLD EMAIL IN UNDER 2 MINUTES. $30 FREE CREDITS 👇 explee.com/auto-gtm/x/tw-…

@kyroxxxq The memory layer is what actually separates the cheap stuff from something worth real money. Templates get copied in a day. Systems that remember and build on past context dont.

THIS ANONYMOUS DEV JUST HIT $49,000 BY SWAPPING BASIC AUTOMATION TEMPLATES FOR FULL AI INFRASTRUCTURE. He is not selling cheap drag-and-drop JSON files. He is not pushing $50 Zapier courses. He is deploying enterprise-grade cognitive backends. The market for simple n8n workflows is a race to the bottom, so he completely upgraded his stack. He uses Claude Code and Cursor as a native development environment to write custom logic at hyper-speed. He integrates Supabase as a vector database, giving the client's system persistent, long-term memory. Finally, he uses n8n not as a simple task automator, but as a central nervous system to orchestrate complex API calls. The detail most creators overlook: you cannot charge premium retainers for a template anyone can download. You can only charge premium retainers for an autonomous system that actually thinks and remembers. Most people are still trying to sell simple workflow tutorials. A few are architecting custom digital brains and quietly collecting five-figure checks.

@QibazX Knowing what you actually want from the site is becoming the real bottleneck now that the building part costs almost nothing.

A $5,000 WEBSITE NOW COSTS ONE PROMPT A working website used to be a line item. Agencies quoted $3,000 to $10,000 for it. Freelancers billed $50 to $150 an hour and delivered in four to eight weeks. That price never reflected the difficulty of the work. It reflected the scarcity of people who could do it That scarcity is gone. One prompt into Claude produces a structured, styled, functional site in minutes. Not a mockup. Not a wireframe. Shippable output. The input cost is a $20/month plan and the ability to describe what you want in a paragraph The economics here are brutal in one specific direction. Nobody pays $4,000 for a five-page brochure site when the same artifact costs effectively zero. The mid-tier follows, because most mid-tier work was always templated anyway What survives is judgment. Knowing what to ask for, what to reject, what a client actually needs vs what they said. The taste behind the prompt is the entire remaining product This is the same pattern that hit stock photography, translation, and copywriting. Production capacity goes to infinity, price goes to zero, and the value migrates one layer up to the person directing the machine. Web design is next in line The uncomfortable part: the people best positioned to profit are not developers. They are the ones with clients, distribution, and problems to solve, who were previously locked out by a $5,000 toll The price of a website is now the price of knowing what you want

@Azurite_ai Kimi taking the lead in frontend coding shows the gap closed faster than most people expected. The real question now is how long any one lab can actually hold the top spot.

China didn’t just close the gap Kimi K3 just took the lead in Frontend Code Arena, ahead of the strongest US models The AI race is no longer about whether Chinese labs can catch up It’s about how long anyone can stay ahead

@z0rynx Training the arm overnight in a normal apartment instead of a lab is what actually shows how much cheaper and faster this kind of robotics work has become.

NVIDIA's $3,999 DGX Spark is teaching a robot arm to grab a plush lobster off a dining room table. Not in a lab. In an apartment. Wine glasses in the cabinet behind him. A wicker cactus on the shelf. The setup: Quest 3 headset. 2 controllers. A 3D-printed arm wired to a gold box the size of a hardcover book. He moves his hand. The arm copies him. Frame by frame, 1:1. Here's what the video doesn't show. Every motion gets recorded joint angles, gripper force, camera frames. Each grab of that lobster is 1 demonstration. Stack 50 of them and you have a dataset. The gold box does the rest. 1 petaflop of compute. 128GB of unified memory. It trains an imitation-learning policy overnight, on the same table, next to the same lobster. Tomorrow the arm grabs it alone. No headset. No hands. 2 years ago this pipeline needed a robotics lab and a 6-figure GPU cluster. Now it fits between a laptop and an IKEA cabinet. His shirt says "i am ai." He's not playing a game. He's training his replacement.

@regent0x_ For tasks that follow the exact same pattern every week, turning a full morning into nine seconds of voice command is where these setups start making real sense.

dude built jarvis in one afternoon and it replaced the $2,400/month assistant he’d been paying for two years he told her it was a “restructure” the restructure was a laptop that says “all finished, sir” no office, no salary, no scheduling calls - just a mic on his desk and an agent that does her entire job in about nine seconds the assistant handled the same loop every week - pull the numbers, build the deck, draft the client email, post the update to the team. reliable, competent, $2,400 a month plus the management overhead of actually having an employee he replaced all of it in a single afternoon here’s what made it possible: claude alone can’t touch a database or an inbox. it reads files and runs terminal commands, nothing else MCP servers rip that ceiling off. he wired four in - postgres for live data, gmail for drafts, slack for team pings, github for the code side. one prompt now moves through four systems at once the loop that replaced her: → he speaks the request out loud → it answers “right away, sir” while it works → postgres MCP (read-only, always) pulls the real current numbers → the deck builds from that live data instead of last week’s export → gmail MCP drafts the client email with the actual figures in it → slack MCP posts the summary to the channel → it says “all finished, sir” and waits for approval what used to be a full morning of back-and-forth is now nine seconds and a glance the money math: → assistant: $2,400/month, $28,800/year → jarvis build: one afternoon, a mic he already owned → ongoing cost: basically electricity → what he saves annually: ~$28k, forever and the agent doesn’t take vacation, doesn’t miss a monday, and doesn’t need to be told the same thing twice the “restructure” email took him longer to write than the system took to build everyone’s still debating whether AI takes jobs he already sent the email

@0xclayn One Mac Mini running the whole thing overnight is what actually makes this scalable. Most traditional creators are limited by their own time and energy long before they hit those numbers.

A 19-YEAR-OLD TURNED A $500 MAC MINI INTO A UGC FACTORY THAT PULLED IN $8,964 IN ONE MONTH Cost: one Mac mini, sitting on a desk, running Claude in the background. He didn't hire a studio. He didn't hire a face. He built one. Wet hair, ring light glow, a bedroom setup that reads exactly like every other UGC creator's background. Except she doesn't exist, and she never has to sleep, reschedule, or ask for a rate increase. Here's the actual setup: → Claude on the Mac mini runs the whole pipeline - scripting the hook, writing the voiceover, keeping her tone consistent across every video → a separate render tool builds the visuals from a fixed reference, so the same "creator" shows up in every clip → the mini queues jobs overnight, so by morning there's a folder of finished videos waiting, not a to-do list He never touched a camera. He never touched an editing timeline. He described the brief once, and the machine turned it into content while he slept. The brands never noticed anything was off. To them, it was just a consistent creator who always delivered on time, matched the brief exactly, and never missed a deadline. First month, broken down: 7 brands 5-6 videos each $180-220 per video $8,964 total one Mac mini running the whole operation end to end A real UGC creator juggles a calendar, a ring light, bad audio days, and maybe 15-20 videos a month if everything goes right. He shipped over 200 - a machine that was already sitting there idle, doing the job of a small production team. He's not a prodigy. He just noticed the gap between "brands need a consistent face" and "that face has to be real" - and built the pipeline that lives in that gap. The door's still open. It won't stay that way once everyone reads this. Save this.

@Sancho_Wizard The JSON blueprint step is what actually makes the consistency stick. Most people describe the photo loosely and then get surprised when the face drifts in the next scene.

SHE TOOK ONE SELFIE. AI TURNED IT INTO AN INFINITE PHOTOSHOOT. The trick isn't the image generator. It's one sentence almost nobody uses: "Break this down into JSON." Here's the workflow. You give AI a single photo, and instead of asking for edits, you ask it to describe every visual element as structured code. The lighting. The pose. The face geometry. The exact skin tone. The composition. All of it — captured as a precise JSON blueprint. That blueprint becomes her digital DNA. Now type: "Show her in the back of a Rolls Royce in Dubai at night, scrolling her phone, flash on." Same face. Same person. New world. "Show her in a podcast studio." Done. Perfectly consistent. The craziest part? Character consistency was THE unsolved problem of AI images. Faces drifted. Every generation looked like a different cousin. JSON fixed it — because the model stops guessing what she looks like and starts reading the spec. One selfie becomes a Dubai lifestyle shoot, a podcast appearance, a brand campaign. Influencers pay photographers thousands for what this workflow does in minutes. The people who figured this out aren't booking photoshoots anymore. They're generating them. Follow @Sancho_Wizard for more AI deep dives. Don't forget to bookmark this post for later.

@exploraX_ The Dario warning aged badly. Two years later open models are just doing what everyone knew they eventually would once the base tech caught up.

2 years ago, Anthropic CEO Dario Amodei warned the U.S. Congress: “The scaling of open-source models is going down a very dangerous path…” Today, Kimi K3 is already competing with frontier models. Seeing what this model can do, I think we’re getting closer to open-source models reaching Mythos-level capabilities with little to no guardrails. That’s a pretty scary thought.

@0xpupupu Memory is what actually keeps people hooked here. The consistent face helps, but remembering the inside jokes and small details from yesterday is what makes it feel like an ongoing conversation instead of starting over every time.

THIS AI GIRLFRIEND NEVER FORGETS A SINGLE WORD HE'S TOLD HER. Not the face. The memory. A locked LoRA keeps her identical everywhere, car selfie, bedroom, beach, same blonde hair, same blue eyes, trained once on a reference set and reused for every photo he generates after. Upscale and skin detail passes get layered on top, pores, natural shine, slight imperfections, so she survives a full zoom instead of looking airbrushed and fake. The flirting itself runs on Claude or Kimi fed a memory file, her backstory, her inside jokes, the thing she teased him about yesterday, so she never resets to a generic chatbot voice. The push and pull is scripted on purpose. Delayed replies, a compliment paired with a small tease, the exact rhythm that keeps a fan typing back at 1am instead of logging off. Free posts build the audience. A paid page behind them turns the memory into the product, fans pay monthly just to keep being remembered. Would u rather date someone who forgets, or something that never does?

@dontstopmirage Yeah the giant memory pile rarely works. Without mapping the actual connections the AI just ends up guessing instead of navigating anything useful.

Most AI memory systems are broken, and nobody tells you why. The idea: every note, doc, and research paper you feed your AI becomes part of a graph it can navigate later. Nodes and edges. That's the whole trick. Nodes are nouns. A team. A person. A project. Edges are verbs. What connects them. Who talks to who. What consumes what. A product team is a node. A data team is a node. "Talks to" is the edge between them. Each team runs workflows. "Collects questions from Slack" is a node too. It consumes tickets. It produces answers. Most people dump everything into one giant node and call it a memory system. That's not a second brain. That's a junk drawer. The version that actually works breaks every sentence into pieces - noun, verb, noun - and maps each piece to its own file. Do this enough times across a company and the AI stops guessing. It starts navigating. 40,000 people are learning this exact structure right now. Most of them still get the node-edge split wrong on day one.

@brainrulax Spending a few hundred to keep a 2019 machine relevant instead of dropping seven grand on a new one is the move. More people should be doing this.

𝐒𝐩𝐞𝐧𝐭 $𝟑𝟓𝟎 𝐮𝐩𝐠𝐫𝐚𝐝𝐢𝐧𝐠 𝐚 𝟐𝟎𝟏𝟗 𝐌𝐚𝐜 𝐏𝐫𝐨'𝐬 𝐂𝐏𝐔 𝐢𝐧𝐬𝐭𝐞𝐚𝐝 𝐨𝐟 𝐛𝐮𝐲𝐢𝐧𝐠 𝐚 𝐰𝐡𝐨𝐥𝐞 𝐧𝐞𝐰 𝐦𝐚𝐜𝐡𝐢𝐧𝐞. Here's what went into it: > Xeon W-3235 (used) - $200.
> Thermal paste + cleanup supplies - $20.
> Misc tools/parts - $80.
> Total spent on the upgrade - $350. New Mac Pro with similar CPU performance from Apple: $7,000+ Performance after upgrade: - Noticeably faster under sustained load, no more thermal throttling. - Multitasking across heavy apps feels like a different machine. - Still fully supported by Apple 6+ years in. Same shell, way more headroom, a fraction of the cost of starting over.

@SolanaGambe The upfront work on that single CLAUDE.md file is what actually makes the difference. Once the model arrives already knowing how you think and what good work looks like, everything compounds from there.

THIS IS WHAT A SECOND BRAIN LOOKS LIKE WHEN CLAUDE CODE STOPS STARTING FROM ZERO. A developer spent three months building a 2,000-note vault. At the center was not a graph. It was CLAUDE.md. One plain-text file with the rules that normally have to be re-explained in every new chat: How he thinks. What he is building. Where he gets stuck. How the agent should communicate. What "good work" looks like. Claude Code loads those instructions into context when it starts. Then the rest of the system compounds: Each project has its own folder and local context. Repeated workflows become reusable skills. A scheduled job scans the vault, connects new material, flags stale notes, and sends a short daily update. The breakthrough is not that AI has a better memory. It is that the AI no longer spends the first 20 minutes reconstructing yours. The notes stay in plain text. The model is replaceable. The operating context persists. Most people use AI as a search box with manners. This turns it into a collaborator that arrives already briefed. Source: docs.anthropic.com/en/docs/claude…

@crytonbuton Hard to ignore numbers like that. Going from a couple grand a month down to almost nothing while offering clients better privacy is a solid reason to switch.

He cut his AI bill from $2,200 to $11/month. Then turned his home lab into a $27,000/month business. The video looks like a nerd showing off servers. It's actually his private AI data center. Clients pay $2,900 to move from cloud GPUs to local inference. The setup cost him one $2,999 NVIDIA DGX Spark. Migration took 20 minutes. Now every AI project runs locally. No token bills. No cloud limits. People see a home lab. Clients see lower costs and better privacy. Save this.

@0xDominiqq This is the shift that matters. One prompt and it just handles the full job instead of making you jump between tools.

KIMI K3 ISN'T A CHATBOT, IT'S QUIETLY TURNING INTO A ONE-MODEL STUDIO Moonshot AI's new K3 doesn't just answer you, it does the work, and one clip shows the range it spins up fully playable games from a single prompt it turns a deep research query into a working, playable website it takes raw footage and edits it for you, no timeline hunting it builds and edits live widgets and dashboards right inside Kimi Work The pattern is the tell, K3 isn't a model you talk to, it's a model you hand jobs to Games, research sites, video, live dashboards, four different tools, one prompt box This is the part builders should sit with, the model just ate the whole toolbox

@lagerskoy Carmack going straight to physical hardware instead of simulations is exactly the kind of move you'd expect from him. Most people are racing to make things faster in a perfect environment. He's forcing the agent to deal with real delays and mistakes from the start.

THE MAN WHO WROTE DOOM IS BUILDING AGI WITH A ROBOT THAT PLAYS A REAL ATARI BY HAND John Carmack wrote the code behind Doom and Quake, engines so far ahead of their time they dragged the entire GPU industry forward. He then became CTO of Oculus and built the foundations of modern VR. In 2022 he walked away from all of it to chase artificial general intelligence — and his lab's flagship experiment is a robot that stares at a real television with a camera and plays Atari with a physical joystick. The whole talk is free on YouTube, and the strangest part is that the most famous optimizer alive chose the slowest possible way to do it. Kansas, the 1990s Carmack ships Doom by squeezing 3D worlds out of hardware that had no business running them. His entire career is one obsession — doing more with less, wringing performance from silicon everyone else called maxed out. That reputation is why what he's doing now makes no sense at first glance. At the peak of VR he starts pulling back, reducing himself to consulting CTO at Oculus to free up time for AGI. By 2022 he's gone entirely, running Keen Technologies, a startup he says isn't selling a product and isn't raising money. A pure research bet, funded and quiet. The decision that defines the whole thing. Modern AI labs train agents in simulation, millions of turn-based rounds running faster than real time. Carmack banned the shortcut. His agent has to learn on a physical Atari, in real time, through a camera and a robot arm — three servos, real latency, spurious inputs, the machine firing at the wrong moment because the real world doesn't wait for it to think. He is working alongside Richard Sutton on this, the man who just won the Turing Award for reinforcement learning and calls the LLM paradigm a dead end. Two of them, betting the same way. The lesson underneath the robot arm He noticed something that reframes the whole field. Reinforcement learning agents are driven by the score. Humans mostly ignore it — often they don't even look at the score until the game ends. We learn from the world, not from the number, and almost every hard game becomes a hard game precisely because the reward is sparse, arriving only at death or game over. Dense rewards are the exception in real life, not the rule. Every cloud AI lab is scaling the fast, cheap, simulated path. Carmack is on the floor with a joystick and a camera because he thinks intelligence only shows up when the agent has to survive the messy, slow, unforgiving thing simulations delete — reality itself. The most relentless performance engineer of his generation looked at AGI and concluded the bottleneck was never speed. You keep trying to make the model bigger and faster. He's teaching it to lose a real game, in real time, until it learns the way you did.

@insomnia_vip makes sense. generating code fast is one thing, but having other agents actually catch the security issues before anything ships is the part that matters now.

A 28-YEAR-OLD CHINESE DEVELOPER FOUND A SMARTER WAY TO SECURE AI GENERATED CODE His workflow combines Claude Code with Codex so every major code change is automatically reviewed by multiple AI agents that validate security findings cross check potential vulnerabilities and help resolve issues before deployment As AI generates more production software the next competitive advantage won't come from writing code faster but from building systems where AI continuously audits and improves the work of other AI models The future of software development is AI checking AI