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someone just open-sourced the entire "loop engineering" playbook. for free. 8.6k stars on github for loop engineering not prompts. loops. everything you need to build autonomous AI systems: → scheduling → memory & state → planning → sub-agents → verification → worktrees → MCP → stop conditions → safety...

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Rahul

130,541 subscribers

62,073 views • 4 days ago •via X (Twitter)

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I use Flot AI because I’m tired of thinking about prompts. Not because I don’t know how — but because I don’t want to stop my work just to phrase things perfectly. Most AI tools still expect you to: pause, think, rewrite the question, tweak the prompt. Flot AI doesn’t. It already has strong built-in prompts for everyday work: rewriting, polishing, translating, summarizing. I just select what I’m working on and ask — no prompt engineering, no overthinking. The best part is that it works in place. Emails, docs, articles — I stay where I am, get the result, keep going. When AI stops demanding “better prompts,” it finally becomes what it should be:a quiet assistant, not another task.
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I use Flot AI because I’m tired of thinking about prompts. Not because I don’t know how — but because I don’t want to stop my work just to phrase things perfectly. Most AI tools still expect you to: pause, think, rewrite the question, tweak the prompt. Flot AI doesn’t. It already has strong built-in prompts for everyday work: rewriting, polishing, translating, summarizing. I just select what I’m working on and ask — no prompt engineering, no overthinking. The best part is that it works in place. Emails, docs, articles — I stay where I am, get the result, keep going. When AI stops demanding “better prompts,” it finally becomes what it should be:a quiet assistant, not another task.

Pushpendra Tripathi

88,769 views • 7 months ago

AI agent usage on SQD Portal is up ~200% in recent weeks. A dev from our community chat was scraping a wallet UI with Hermes. Mid-task, DeepSeek reasoned its way out of it: "I can use SQD Portal's Hyperliquid fills data directly — much more complete than scraping a UI with infinite scroll." No prompt engineering. The model just chose the better and faster path. This is the loop we wanted: Agents pick SQD because it's faster → devs see agents picking SQD → devs ship faster → more agents pick SQD The picks-and-shovels moment for AI x onchain is here.
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AI agent usage on SQD Portal is up ~200% in recent weeks. A dev from our community chat was scraping a wallet UI with Hermes. Mid-task, DeepSeek reasoned its way out of it: "I can use SQD Portal's Hyperliquid fills data directly — much more complete than scraping a UI with infinite scroll." No prompt engineering. The model just chose the better and faster path. This is the loop we wanted: Agents pick SQD because it's faster → devs see agents picking SQD → devs ship faster → more agents pick SQD The picks-and-shovels moment for AI x onchain is here.

sqd.ai

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You think you’ve seen impressive engineering… and then this happens. It is honestly incredible to watch this level of aerospace engineering brought down to such a scale. This is not just a flight; it is a masterclass in stabilization, thermal management, and precision guidance. Seeing a vehicle perform a vertical launch, sustain extreme conditions, and return with intact telemetry data is what true innovation looks like. We are witnessing the democratization of space access right here in a backyard. Massive credit to the team for pushing these boundaries and showing us exactly what is possible when passion meets advanced physics. A truly legendary display of technical grit. Credit: projecthorizon_mark
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You think you’ve seen impressive engineering… and then this happens. It is honestly incredible to watch this level of aerospace engineering brought down to such a scale. This is not just a flight; it is a masterclass in stabilization, thermal management, and precision guidance. Seeing a vehicle perform a vertical launch, sustain extreme conditions, and return with intact telemetry data is what true innovation looks like. We are witnessing the democratization of space access right here in a backyard. Massive credit to the team for pushing these boundaries and showing us exactly what is possible when passion meets advanced physics. A truly legendary display of technical grit. Credit: projecthorizon_mark

Cosmos Archive

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Excited to launch a new way to upskill with AI agents. This is how we are making it possible for anyone to learn to build with coding agents. To start, we are launching 4 new hands-on labs on the following topics: - Agent Skills - Agentic Image Generation - 30 Days of Hermes Agents - Prompt Engineering with Agents I am confident that with our new DAIR.AI platform, anyone can learn to become a top AI builder by building and acquiring highly-demanded AI skills. And there is a lot more landing in the coming weeks.
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Excited to launch a new way to upskill with AI agents. This is how we are making it possible for anyone to learn to build with coding agents. To start, we are launching 4 new hands-on labs on the following topics: - Agent Skills - Agentic Image Generation - 30 Days of Hermes Agents - Prompt Engineering with Agents I am confident that with our new DAIR.AI platform, anyone can learn to become a top AI builder by building and acquiring highly-demanded AI skills. And there is a lot more landing in the coming weeks.

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Lars Moravy, VP of Vehicle Engineering on why most cars don’t allow you to use your rear-view camera in motion: “I think it’s probably because they don’t have an integrated MCU, they’re relying on third-party to give them the camera, then they have to go access that software. Whereas for us, it’s all just one thing” Everything is integrated at Tesla.
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Lars Moravy, VP of Vehicle Engineering on why most cars don’t allow you to use your rear-view camera in motion: “I think it’s probably because they don’t have an integrated MCU, they’re relying on third-party to give them the camera, then they have to go access that software. Whereas for us, it’s all just one thing” Everything is integrated at Tesla.

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Today is Kotori’s birthday! At Millennium’s Engineering Club, the moment someone asks “Why,” Kotori’s lecture is already in session. From ultrahigh frequencies to robots, gear, and even the secret of those teardrop glasses, she always works hard to make everything “easy to understand.” So just for today, be the one to ask first What’s her favorite thing to explain, and what kind of birthday wishes does she want to hear. I hope today becomes a day where she can smile and explain as much as she likes. #BlueArchive
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Today is Kotori’s birthday! At Millennium’s Engineering Club, the moment someone asks “Why,” Kotori’s lecture is already in session. From ultrahigh frequencies to robots, gear, and even the secret of those teardrop glasses, she always works hard to make everything “easy to understand.” So just for today, be the one to ask first What’s her favorite thing to explain, and what kind of birthday wishes does she want to hear. I hope today becomes a day where she can smile and explain as much as she likes. #BlueArchive

BlueArchive

39,034 views • 6 months ago

After 200+ days of obsessive testing, I finally built the most effective AI Automation playbook on the internet. (worth $100K+ in system builds) while I was watching people panic about "AI taking over," I was quietly building the systems that actually use it to print money. these are the exact infrastructures we've battle-tested to drive: → 1,000s of qualified leads → 4M+ impressions → 30K+ followers → real revenue (not just vanity metrics) here's what the full stack includes: – world-class n8n AI agents that never break – MCP-powered content automation engines – follow-up sales agents that actually close – multi-channel outreach flows – lead capture, enrichment & activation systems – CRM sync + sales infrastructure – state-of-the-art workflow architectures this isn't some recycled template dump. these are hand-crafted, production-grade systems running in my business & with clients pulling real numbers. it's like having the engineering team from SpaceX — but for automations and with way less rocket explosions. all built for scale. all tested in live environments. all documented with step-by-step setup guides. agencies charge $25K-$50K just to build one of these systems. you're getting the entire war chest. Comment "AGENT" + repost this + follow me I'll DM you the complete playbook in the next hour skip this, and go back to copy-pasting YouTube tutorials that break after 3 days.
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After 200+ days of obsessive testing, I finally built the most effective AI Automation playbook on the internet. (worth $100K+ in system builds) while I was watching people panic about "AI taking over," I was quietly building the systems that actually use it to print money. these are the exact infrastructures we've battle-tested to drive: → 1,000s of qualified leads → 4M+ impressions → 30K+ followers → real revenue (not just vanity metrics) here's what the full stack includes: – world-class n8n AI agents that never break – MCP-powered content automation engines – follow-up sales agents that actually close – multi-channel outreach flows – lead capture, enrichment & activation systems – CRM sync + sales infrastructure – state-of-the-art workflow architectures this isn't some recycled template dump. these are hand-crafted, production-grade systems running in my business & with clients pulling real numbers. it's like having the engineering team from SpaceX — but for automations and with way less rocket explosions. all built for scale. all tested in live environments. all documented with step-by-step setup guides. agencies charge $25K-$50K just to build one of these systems. you're getting the entire war chest. Comment "AGENT" + repost this + follow me I'll DM you the complete playbook in the next hour skip this, and go back to copy-pasting YouTube tutorials that break after 3 days.

Aryan Mahajan

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Today we're opening offices in Madrid, Milan, and Paris, and building a dedicated engineering hub in London. The demand came before we did. Organisations across Spain, Italy, and France were already running Legora on their most complex work before we had a single person on the ground there. When customers adopt you in a market you haven't entered yet, you listen. These are markets that sit at the centre of European M&A, infrastructure, and cross-border regulatory work. The matters are hard, and the demand for AI that can actually handle that complexity is real. That's the work we're built for. London becomes the third pillar of our engineering org, alongside Stockholm and New York. The engineers who understand how AI applies in regulated, professional settings are concentrated there, shaped by proximity to some of the most demanding legal and financial institutions in the world. That's exactly the problem we're solving, and exactly the team we want building it. Sixteen cities. Four continents. Seven hundred EMEA hires in the next 6 to 12 months. All of it pointed at one thing: making lawyers 10x better at what they do. To be part of it, take a look at
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Today we're opening offices in Madrid, Milan, and Paris, and building a dedicated engineering hub in London. The demand came before we did. Organisations across Spain, Italy, and France were already running Legora on their most complex work before we had a single person on the ground there. When customers adopt you in a market you haven't entered yet, you listen. These are markets that sit at the centre of European M&A, infrastructure, and cross-border regulatory work. The matters are hard, and the demand for AI that can actually handle that complexity is real. That's the work we're built for. London becomes the third pillar of our engineering org, alongside Stockholm and New York. The engineers who understand how AI applies in regulated, professional settings are concentrated there, shaped by proximity to some of the most demanding legal and financial institutions in the world. That's exactly the problem we're solving, and exactly the team we want building it. Sixteen cities. Four continents. Seven hundred EMEA hires in the next 6 to 12 months. All of it pointed at one thing: making lawyers 10x better at what they do. To be part of it, take a look at

Max Junestrand

70,251 views • 1 month ago

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A serious perps funding bot isn't a "tool" anymore. it's a $20k-$250k execution system perps funding bots are becoming infrastructure, not "alpha tools" simple version: > you don't predict price > you hedge exposure > you collect funding between longs & shorts but the real story is execution because in practice: > one leg fills, the other doesn’t → you’re suddenly directional > sessions expire → bot goes blind > positions exist but systems don’t recognize them → broken hedges that's why this space is shifting from "strategy" → "execution engineering" pricing reflects it: - MVP bots: $5k-$20k - production systems: $20k-$80k - institutional-grade infra: $80k-$250k+ who actually uses this: - crypto hedge funds (market-neutral desks) - prop firms (funding + basis strategies) - indie quants building yield systems - infra-native devs treating trading as systems engineering - builders using Claude to prototype quant infra faster and the key takeaway: in funding strategies, the edge is rarely the signal it's staying correctly hedged under real exchange conditions paper trading doesn't show any of this only live execution does

hammertime

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ELON: BRANDS ARE BUILT IN FACTORIES, NOT BILLBOARDS Branding? Please. That’s just the echo of great engineering! Brand isn’t crafted in ad agencies - it’s hammered out on factory floors. The Blueprint for Hype: •⁠ ⁠Build something people can’t stop talking about •⁠ ⁠Let the next release sell itself •⁠ ⁠Keep stacking wins - the trust snowballs From Model S to Cybertruck, from Falcon 1 to Starship - Tesla and SpaceX didn’t buy trust, they earned it one product at a time! Source: Elon Musk, Elon Clips, Tesla Shareholder Meeting, June 4, 2013
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ELON: BRANDS ARE BUILT IN FACTORIES, NOT BILLBOARDS Branding? Please. That’s just the echo of great engineering! Brand isn’t crafted in ad agencies - it’s hammered out on factory floors. The Blueprint for Hype: •⁠ ⁠Build something people can’t stop talking about •⁠ ⁠Let the next release sell itself •⁠ ⁠Keep stacking wins - the trust snowballs From Model S to Cybertruck, from Falcon 1 to Starship - Tesla and SpaceX didn’t buy trust, they earned it one product at a time! Source: Elon Musk, Elon Clips, Tesla Shareholder Meeting, June 4, 2013

Mario Nawfal

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I believe solving robotics = 90% engineering + 10% research vision. Project GR00T is NVIDIA's moonshot initiative to build physical AGI for humanoid robots. The GEAR Lab is assembling a crack team right now. Join us! Openings: - Sr. Research Engineer, Robotics Systems - Sr. RE, Reinforcement Learning - Sr. RE, Foundation Model Training Infrastructure - Sr. RE, Simulation - Sr. RE, ML Data Pipelines - Research Scientist - Research Intern (both part-time and summer full-time in 2025) For the Sr. positions, we strongly prefer candidates with many years of engineering experience at robotics/autonomous driving companies, or MLOps/large-scale AI teams at big techs. For interns, we welcome ace robotics hackers anywhere! Show me your past works. Job links in the thread. Apply today! Your resumes will be my best Christmas gifts:
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I believe solving robotics = 90% engineering + 10% research vision. Project GR00T is NVIDIA's moonshot initiative to build physical AGI for humanoid robots. The GEAR Lab is assembling a crack team right now. Join us! Openings: - Sr. Research Engineer, Robotics Systems - Sr. RE, Reinforcement Learning - Sr. RE, Foundation Model Training Infrastructure - Sr. RE, Simulation - Sr. RE, ML Data Pipelines - Research Scientist - Research Intern (both part-time and summer full-time in 2025) For the Sr. positions, we strongly prefer candidates with many years of engineering experience at robotics/autonomous driving companies, or MLOps/large-scale AI teams at big techs. For interns, we welcome ace robotics hackers anywhere! Show me your past works. Job links in the thread. Apply today! Your resumes will be my best Christmas gifts:

Jim Fan

103,177 views • 1 year ago

The #NMIAReadyToFly Is not merely a second airport for Mumbai! it is the closest global cargo gateway yet for Pune’s industrial powerhouse. Think about the scale of what this changes: • Pune–Chakan–Ranjangaon belt = India’s largest auto & engineering hub • Massive clusters in EVs, components, precision engineering, pharma & electronics • Today, exports fight congestion + time-loss through a saturated Mumbai airport • NMIA now creates a reliable, high-throughput cargo corridor ~120 km away • Built to scale cargo handling from 0.5 million → 3+ million tonnes annually For manufacturers, this means: Faster export turnarounds, lower logistics friction, stronger global competitiveness. This is not just an airport launch. It is the moment Western India quietly becomes one of Asia’s most powerful logistics + manufacturing ecosystems. Well done Adani Group and Gautam Adani Sir! Many Congratulations! 🙌🏻
0:12

The #NMIAReadyToFly Is not merely a second airport for Mumbai! it is the closest global cargo gateway yet for Pune’s industrial powerhouse. Think about the scale of what this changes: • Pune–Chakan–Ranjangaon belt = India’s largest auto & engineering hub • Massive clusters in EVs, components, precision engineering, pharma & electronics • Today, exports fight congestion + time-loss through a saturated Mumbai airport • NMIA now creates a reliable, high-throughput cargo corridor ~120 km away • Built to scale cargo handling from 0.5 million → 3+ million tonnes annually For manufacturers, this means: Faster export turnarounds, lower logistics friction, stronger global competitiveness. This is not just an airport launch. It is the moment Western India quietly becomes one of Asia’s most powerful logistics + manufacturing ecosystems. Well done Adani Group and Gautam Adani Sir! Many Congratulations! 🙌🏻

Chandrashekhar Dhage

150,314 views • 7 months ago

Don't train the model, evolve the harness. I read a brilliant blog post from Hugging Face where they took a frozen open model scoring 0% on a hard legal agent benchmark, left its weights alone, and let an automated loop rewrite only the code around it. That code layer is the harness, the runtime wrapper that feeds the model context, runs its tool calls, and decides when a run ends. By the time the loop finished, the system had essentially matched Sonnet 4.6 on the benchmark's headline metric, at roughly 7x lower cost per task. Zero weights changed. The gain existed because of where the model was failing. The judge only grades files saved in the right place under the exact requested filename, and the model kept doing the legal analysis correctly, then saving it under the wrong name, dropping it in a scratch folder, or never writing it at all. So the 0% was never measuring legal reasoning. It was measuring the harness. Hand-tuning that layer is slow and model-specific, so they automated it. A Claude proposer adds exactly one mechanism per iteration, and an outer loop keeps it only if it clearly beats the current best, so accepted mechanisms compound. What the loop discovered says a lot about where agents actually fail. → The biggest single gain was file handling, not intelligence. An automatic step that lands the deliverable exactly where the judge expects it beat every prompt change, with zero extra model tokens. → Code fixes transferred across models, prompt playbooks did not. The same harness lifted a smaller model from the same family by 14 points, but the tuned prompts hurt a different model family on tasks it could already finish. → The harness mattered more than anything else. Same model, same judge, same tasks, and five different harnesses scored anywhere between 3.5% and 80.1%. The gains do eventually flatten, and the remaining misses look like real capability gaps. At some point the wrapper runs out of tricks and the model has to carry the work. But the lesson holds. A benchmark score measures the model and its harness together, and until the harness is fixed, it's impossible to know which one failed. I highly recommend reading this: I also wrote a deep dive on agent harness engineering a while back, covering the orchestration loop, tools, memory, context management, and everything that turns a stateless LLM into a capable agent. The article is quoted below.
0:18

Don't train the model, evolve the harness. I read a brilliant blog post from Hugging Face where they took a frozen open model scoring 0% on a hard legal agent benchmark, left its weights alone, and let an automated loop rewrite only the code around it. That code layer is the harness, the runtime wrapper that feeds the model context, runs its tool calls, and decides when a run ends. By the time the loop finished, the system had essentially matched Sonnet 4.6 on the benchmark's headline metric, at roughly 7x lower cost per task. Zero weights changed. The gain existed because of where the model was failing. The judge only grades files saved in the right place under the exact requested filename, and the model kept doing the legal analysis correctly, then saving it under the wrong name, dropping it in a scratch folder, or never writing it at all. So the 0% was never measuring legal reasoning. It was measuring the harness. Hand-tuning that layer is slow and model-specific, so they automated it. A Claude proposer adds exactly one mechanism per iteration, and an outer loop keeps it only if it clearly beats the current best, so accepted mechanisms compound. What the loop discovered says a lot about where agents actually fail. → The biggest single gain was file handling, not intelligence. An automatic step that lands the deliverable exactly where the judge expects it beat every prompt change, with zero extra model tokens. → Code fixes transferred across models, prompt playbooks did not. The same harness lifted a smaller model from the same family by 14 points, but the tuned prompts hurt a different model family on tasks it could already finish. → The harness mattered more than anything else. Same model, same judge, same tasks, and five different harnesses scored anywhere between 3.5% and 80.1%. The gains do eventually flatten, and the remaining misses look like real capability gaps. At some point the wrapper runs out of tricks and the model has to carry the work. But the lesson holds. A benchmark score measures the model and its harness together, and until the harness is fixed, it's impossible to know which one failed. I highly recommend reading this: I also wrote a deep dive on agent harness engineering a while back, covering the orchestration loop, tools, memory, context management, and everything that turns a stateless LLM into a capable agent. The article is quoted below.

Akshay 🚀

243,774 views • 20 days ago

Karpathy's Agentic Engineering finally has proper tooling! (built by Google) Karpathy defined agentic engineering as the discipline that separates production agent work from vibe coding. The core skills he listed were spec design, eval loops, and security oversight. The problem has been that practicing this still requires a different tool for every phase: - editor for code - a terminal for scaffolding - a browser for testing - a cloud console for deployment - and a separate framework for evals. Every transition is a context switch. The solution to production-grade Agentic Engineering is now actually implemented in Google’s Agents CLI. It covers the entire workflow in one place for scaffolding, evaluating, and deploying ADK agents. One setup command injects 7 ADK-specific skills into a coding agent's context, which lets it handle scaffolding, evals, deployment, and enterprise registration through natural language. I tested this end-to-end by building a RAG agent from scratch using Claude Code. It scaffolded the full project from the ADK agentic_rag template, generated 20 eval scenarios with LLM-as-judge scoring, and returned a quantitative scorecard. Finally, it also deployed everything to Agent Runtime and registered the agent to Gemini Enterprise, so the entire org can discover and use it. The video below shows this in action, and I worked with the Google Cloud team to put this together. Agents CLI GitHub repo → (don't forget to star it ⭐ ) I wrote up the full build covering all six steps from install to enterprise registration. It includes the eval scorecard, the instruction loophole the eval caught before deployment, and what the deployment process actually looks like end-to-end. Read it below.
0:15

Karpathy's Agentic Engineering finally has proper tooling! (built by Google) Karpathy defined agentic engineering as the discipline that separates production agent work from vibe coding. The core skills he listed were spec design, eval loops, and security oversight. The problem has been that practicing this still requires a different tool for every phase: - editor for code - a terminal for scaffolding - a browser for testing - a cloud console for deployment - and a separate framework for evals. Every transition is a context switch. The solution to production-grade Agentic Engineering is now actually implemented in Google’s Agents CLI. It covers the entire workflow in one place for scaffolding, evaluating, and deploying ADK agents. One setup command injects 7 ADK-specific skills into a coding agent's context, which lets it handle scaffolding, evals, deployment, and enterprise registration through natural language. I tested this end-to-end by building a RAG agent from scratch using Claude Code. It scaffolded the full project from the ADK agentic_rag template, generated 20 eval scenarios with LLM-as-judge scoring, and returned a quantitative scorecard. Finally, it also deployed everything to Agent Runtime and registered the agent to Gemini Enterprise, so the entire org can discover and use it. The video below shows this in action, and I worked with the Google Cloud team to put this together. Agents CLI GitHub repo → (don't forget to star it ⭐ ) I wrote up the full build covering all six steps from install to enterprise registration. It includes the eval scorecard, the instruction loophole the eval caught before deployment, and what the deployment process actually looks like end-to-end. Read it below.

Akshay 🚀

255,810 views • 24 days ago

there are four types of agent loops. most people only know one. loop engineering is a choice between four structures, each handing off one more job than the last. every one answers two questions: what starts a run, and what ends it. hand-run, you answer both yourself, every time. 1) turn-based → you prompt, it acts, you review, you prompt again. both jobs stay with you. use when requirements are still forming. 2) goal-based → "/goal hit Lighthouse 90, stop after 5 tries." an evaluator checks, a no sends it back. use when the outcome is measurable but the path isn't. 3) time-based → a clock fires, it runs "check the PR, fix CI," then waits. /loop local, /schedule survives a closed laptop. use for recurring work. 4) proactive → no human present. it watches a channel, spawns triage, fix, and a reviewer, closes the task itself. use for standing duties you can't predict. not which one is most advanced. whether your task is exploratory, measurable, recurring, or standing. the more you hand off, the less you babysit. full breakdown in the article below.
0:10

there are four types of agent loops. most people only know one. loop engineering is a choice between four structures, each handing off one more job than the last. every one answers two questions: what starts a run, and what ends it. hand-run, you answer both yourself, every time. 1) turn-based → you prompt, it acts, you review, you prompt again. both jobs stay with you. use when requirements are still forming. 2) goal-based → "/goal hit Lighthouse 90, stop after 5 tries." an evaluator checks, a no sends it back. use when the outcome is measurable but the path isn't. 3) time-based → a clock fires, it runs "check the PR, fix CI," then waits. /loop local, /schedule survives a closed laptop. use for recurring work. 4) proactive → no human present. it watches a channel, spawns triage, fix, and a reviewer, closes the task itself. use for standing duties you can't predict. not which one is most advanced. whether your task is exploratory, measurable, recurring, or standing. the more you hand off, the less you babysit. full breakdown in the article below.

Hanako

471,048 views • 8 days ago

youtube is paying $8,217 a month to a channel with zero humans. no face. just 6 AI tools publishing anime on autopilot twice a week and youtube has no idea the algorithm doesn't check who made the video. it checks one number: how long people keep watching that's the entire game an 8-hour lofi anime stream plays on loop. one upload turns into hundreds of hours of watchtime every month at $3-8 RPM that's $2,400-6,400 from a single file the pipeline runs itself claude writes the script. midjourney draws the frames. runway animates. elevenlabs voices it. suno writes the soundtrack. assembles and publishes humans in the process: zero from prompt to a finished 12-minute episode: 2 hours. from episode to youtube: zero one channel. $8,217 last month article below - every prompt for every step most people ask "will AI take my job". better question - why are you still trading hours for money when a pipeline trades prompts for watchtime
0:10

youtube is paying $8,217 a month to a channel with zero humans. no face. just 6 AI tools publishing anime on autopilot twice a week and youtube has no idea the algorithm doesn't check who made the video. it checks one number: how long people keep watching that's the entire game an 8-hour lofi anime stream plays on loop. one upload turns into hundreds of hours of watchtime every month at $3-8 RPM that's $2,400-6,400 from a single file the pipeline runs itself claude writes the script. midjourney draws the frames. runway animates. elevenlabs voices it. suno writes the soundtrack. assembles and publishes humans in the process: zero from prompt to a finished 12-minute episode: 2 hours. from episode to youtube: zero one channel. $8,217 last month article below - every prompt for every step most people ask "will AI take my job". better question - why are you still trading hours for money when a pipeline trades prompts for watchtime

Ventry

118,212 views • 2 months ago

SOMEONE BUILT AN OPEN-SOURCE JARVIS WITH 9 AGENTS AND 5 MEMORY BACKENDS AND YOUR DATA NEVER LEAVES YOUR DEVICE Every time you message ChatGPT or Claude your data hits a server you don't control, gets processed by infrastructure you're paying for and comes back with zero guarantee of what happened in between. OpenJarvis runs the entire stack locally - 9 agent types, 5 memory backends, a learning loop that gets smarter every day and a morning digest that connects to Google Drive and surfaces what matters before you open a single app. Most AI tools are exactly as dumb on day 100 as they were on day 1 because they forget everything when the window closes - this one indexes your documents once and automatically injects relevant context into every prompt forever. Custom agent setup for a client is $500-2,000 one time and AI infrastructure retainer is $300-800 a month - and your cost is one afternoon and an open source repo. The repo is free. The advantage it creates is not.
0:13

SOMEONE BUILT AN OPEN-SOURCE JARVIS WITH 9 AGENTS AND 5 MEMORY BACKENDS AND YOUR DATA NEVER LEAVES YOUR DEVICE Every time you message ChatGPT or Claude your data hits a server you don't control, gets processed by infrastructure you're paying for and comes back with zero guarantee of what happened in between. OpenJarvis runs the entire stack locally - 9 agent types, 5 memory backends, a learning loop that gets smarter every day and a morning digest that connects to Google Drive and surfaces what matters before you open a single app. Most AI tools are exactly as dumb on day 100 as they were on day 1 because they forget everything when the window closes - this one indexes your documents once and automatically injects relevant context into every prompt forever. Custom agent setup for a client is $500-2,000 one time and AI infrastructure retainer is $300-800 a month - and your cost is one afternoon and an open source repo. The repo is free. The advantage it creates is not.

Cortex

11,374 views • 2 months ago

🇺🇸 TESLA'S MODEL Y DRIVES ITSELF STRAIGHT TO SHIPPING Straight from Tesla's Giga Texas factory, a fresh Model Y rolled right off the production line with 0 people inside and hauled itself over to the shipping yard like it was no big deal. That's the kind of future-tech flex that's got everyone buzzing about autonomous everything. No remote babysitting, just pure AI handling the wheel from assembly to parking lot. Elon and crew are hyping it as a game-changer for efficiency: faster shipping, fewer factory fumbles, and a taste of what's coming for customer deliveries. This isn't just cool demo stuff, it's Tesla cranking up production on their top-seller, all while dodging old-school bottlenecks. Source: TeslaNorth, Interesting Engineering, Tesla, TeslaownersSV
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🇺🇸 TESLA'S MODEL Y DRIVES ITSELF STRAIGHT TO SHIPPING Straight from Tesla's Giga Texas factory, a fresh Model Y rolled right off the production line with 0 people inside and hauled itself over to the shipping yard like it was no big deal. That's the kind of future-tech flex that's got everyone buzzing about autonomous everything. No remote babysitting, just pure AI handling the wheel from assembly to parking lot. Elon and crew are hyping it as a game-changer for efficiency: faster shipping, fewer factory fumbles, and a taste of what's coming for customer deliveries. This isn't just cool demo stuff, it's Tesla cranking up production on their top-seller, all while dodging old-school bottlenecks. Source: TeslaNorth, Interesting Engineering, Tesla, TeslaownersSV

Mario Nawfal

138,234 views • 8 months ago

🎙️ "We intend for Bun to be a drop-in replacement for the existing Node.js ecosystem. Most of our engineering time is not actually spent on new features — it's spent on Node.js compatibility". Join our conversation with Jarred Sumner — creator of Bun, a fast all-in-one toolkit for JavaScript & TypeScript runtime and tooling. The host is Patrick Akil, Beyond Coding podcast host. In this chat with Jarred, we explore: ▪️ Why Bun was created and what makes it exciting ▪️ Compatibility with Node.js ▪️ Upcoming features and Bun’s future ▪️ How AI is shaping developer workflows — and more! 📺 Watch now:
0:17

🎙️ "We intend for Bun to be a drop-in replacement for the existing Node.js ecosystem. Most of our engineering time is not actually spent on new features — it's spent on Node.js compatibility". Join our conversation with Jarred Sumner — creator of Bun, a fast all-in-one toolkit for JavaScript & TypeScript runtime and tooling. The host is Patrick Akil, Beyond Coding podcast host. In this chat with Jarred, we explore: ▪️ Why Bun was created and what makes it exciting ▪️ Compatibility with Node.js ▪️ Upcoming features and Bun’s future ▪️ How AI is shaping developer workflows — and more! 📺 Watch now:

GitNation Foundation

10,877 views • 1 year ago

🚨 The future of AI isn’t bigger models. It’s smarter agents that act, not just respond. GPT-5 came, but it didn’t wow. The real revolution is the Agent Era, where speed, engineering, and real-world execution win. Meet GenFlow 2.0 by Baidu Wenku, the most advanced general-purpose agent right now, and the first to let you intervene mid-generation, a capability not available in GPT or Manus. ➡️ Interrupt and edit tasks mid-generation (exclusive to GenFlow) ➡️ Run 100+ agents simultaneously ➡️ Deep data integration with live workflow control ➡️ Built for product, not just research China is outpacing the U.S. in AI productization. This isn’t another ChatGPT clone. It could be ChatGPT’s first real rival. I built a full investor pitch deck in under 3 minutes, editing content live as it generated. The center of AI innovation is shifting. 👇 Watch AI stop waiting for commands and start working with you.
0:13

🚨 The future of AI isn’t bigger models. It’s smarter agents that act, not just respond. GPT-5 came, but it didn’t wow. The real revolution is the Agent Era, where speed, engineering, and real-world execution win. Meet GenFlow 2.0 by Baidu Wenku, the most advanced general-purpose agent right now, and the first to let you intervene mid-generation, a capability not available in GPT or Manus. ➡️ Interrupt and edit tasks mid-generation (exclusive to GenFlow) ➡️ Run 100+ agents simultaneously ➡️ Deep data integration with live workflow control ➡️ Built for product, not just research China is outpacing the U.S. in AI productization. This isn’t another ChatGPT clone. It could be ChatGPT’s first real rival. I built a full investor pitch deck in under 3 minutes, editing content live as it generated. The center of AI innovation is shifting. 👇 Watch AI stop waiting for commands and start working with you.

SARAH

122,478 views • 11 months ago