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A GERMAN DEVELOPER REPLACED HIS ENTIRE DEV TEAM WITH KIMI K2.6, VISUALIZED EVERYTHING IN OBSIDIAN AND NOW MAKES $80,000/MONTH SOLO 1 trillion parameters, 32 billion activated per token and a SWE-Bench score of 65.8 - Kimi K2.6 reads the entire client codebase, understands the architecture, writes production code and...

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Noisy

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845,855 görüntüleme • 21 gün önce •via X (Twitter)

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KIMI K2.6 JUST CRUSHED GPT-5 AND A SINGLE PERSON CAN NOW POTENTIALLY BUILD AN $80K/MONTH BUSINESS WITH 300 AI AGENTS AND JUST $500 IN OVERHEAD The video attached is proof that almost everyone missed Kimi K2 Thinking didn’t just score 44.9% on Humanity’s Last Exam, it outperformed GPT-5 (41.7%), Claude, and every other major model across multiple benchmarks It’s open source Over a trillion parameters, trained for just $4.6M Runs locally on a Mac Studio and in the demo, it turns a 100-page PDF into a fully designed PowerPoint presentation in under two minutes while other models are still thinking In the article below, the author lays out a clear blueprint for turning this into a real business: > 300 parallel sub-agents running up to 4000 steps per execution - research, coding, analysis and visual creation all happen simultaneously > 65.8% on SWE-Bench solving real GitHub engineering tasks end-to-end with little to no human intervention > Skill injection through simple .md files - instant vertical specialization (HIPAA compliance, financial regulations, Shopify workflows and more) > Automated client acquisition: monitor job listings for “Data Analyst” or “Automation Engineer” roles and pitch an AI solution before companies even start hiring The math is simple: A $10k project Traditional agency → salaries, office costs, QA, project management and overhead eat most of the profit AI agency powered by Kimi → roughly $500 in operating costs plus one operator managing client relationships = the potential for 72k$+ monthly profit at scale Read the article Save this post Start building AI-native agencies while everyone else is still doing things the old way
0:43

KIMI K2.6 JUST CRUSHED GPT-5 AND A SINGLE PERSON CAN NOW POTENTIALLY BUILD AN $80K/MONTH BUSINESS WITH 300 AI AGENTS AND JUST $500 IN OVERHEAD The video attached is proof that almost everyone missed Kimi K2 Thinking didn’t just score 44.9% on Humanity’s Last Exam, it outperformed GPT-5 (41.7%), Claude, and every other major model across multiple benchmarks It’s open source Over a trillion parameters, trained for just $4.6M Runs locally on a Mac Studio and in the demo, it turns a 100-page PDF into a fully designed PowerPoint presentation in under two minutes while other models are still thinking In the article below, the author lays out a clear blueprint for turning this into a real business: > 300 parallel sub-agents running up to 4000 steps per execution - research, coding, analysis and visual creation all happen simultaneously > 65.8% on SWE-Bench solving real GitHub engineering tasks end-to-end with little to no human intervention > Skill injection through simple .md files - instant vertical specialization (HIPAA compliance, financial regulations, Shopify workflows and more) > Automated client acquisition: monitor job listings for “Data Analyst” or “Automation Engineer” roles and pitch an AI solution before companies even start hiring The math is simple: A $10k project Traditional agency → salaries, office costs, QA, project management and overhead eat most of the profit AI agency powered by Kimi → roughly $500 in operating costs plus one operator managing client relationships = the potential for 72k$+ monthly profit at scale Read the article Save this post Start building AI-native agencies while everyone else is still doing things the old way

Bonsai 🌳

21,487 görüntüleme • 12 gün önce

Anthropic's in trouble, again. The entire Claude experience is now available at 1/6th the price. Kimi now does everything Claude does, powered by K2.6, a 1-trillion-parameter MoE model that activates only 32B parameters per token. It covers all three features Claude has (Chat, Code, and Cowork): 1) Kimi Chat runs in four modes - Instant for fast responses - Thinking for deep reasoning - Agent for multi-step execution - and Agent Swarm for parallel workloads. There's a 262K context window across all of them. 2) Kimi Code is the open-source CLI coding agent with K2.6 as the default backend. K2.6 ranked #1 on OpenRouter's programming leaderboard by weekly usage. 3) Kimi Agent is the Cowork equivalent. It generates: - full websites with database and auth - presentation decks (editable PPTX output) - spreadsheets with formulas and charts - word docs and structured research reports. On top of this, Kimi K2.6 is also trained to decompose tasks into up to 300 parallel sub-agents. This helps it retain coherence even across 4,000+ tool calls in a single run, with sessions sustaining up to 13 hours. On SWE-Bench Pro: - Kimi K2.6 → 58.6 - GPT-5.4 xhigh → 57.7 - Gemini 3.1 Pro → 54.2 - Claude Opus 4.6 → 53.4 Kimi K2.6 model is open weights and self-hostable on 4x H100s in INT4. Find the link to the HuggingFace model page in the replies!
0:41

Anthropic's in trouble, again. The entire Claude experience is now available at 1/6th the price. Kimi now does everything Claude does, powered by K2.6, a 1-trillion-parameter MoE model that activates only 32B parameters per token. It covers all three features Claude has (Chat, Code, and Cowork): 1) Kimi Chat runs in four modes - Instant for fast responses - Thinking for deep reasoning - Agent for multi-step execution - and Agent Swarm for parallel workloads. There's a 262K context window across all of them. 2) Kimi Code is the open-source CLI coding agent with K2.6 as the default backend. K2.6 ranked #1 on OpenRouter's programming leaderboard by weekly usage. 3) Kimi Agent is the Cowork equivalent. It generates: - full websites with database and auth - presentation decks (editable PPTX output) - spreadsheets with formulas and charts - word docs and structured research reports. On top of this, Kimi K2.6 is also trained to decompose tasks into up to 300 parallel sub-agents. This helps it retain coherence even across 4,000+ tool calls in a single run, with sessions sustaining up to 13 hours. On SWE-Bench Pro: - Kimi K2.6 → 58.6 - GPT-5.4 xhigh → 57.7 - Gemini 3.1 Pro → 54.2 - Claude Opus 4.6 → 53.4 Kimi K2.6 model is open weights and self-hostable on 4x H100s in INT4. Find the link to the HuggingFace model page in the replies!

Avi Chawla

108,352 görüntüleme • 1 ay önce

THIS CHINESE DEVELOPER VISUALIZED WHAT 300 KIMI K2.6 AGENTS LOOK LIKE IN ACTION - AND IT LOOKS EXACTLY LIKE A BRAIN WORKING FOR YOU every line on screen is a connection firing in real time - hundreds of neurons across multiple layers, activations lighting up, signals passing through the network simultaneously in both directions this is not a diagram and not a concept - this is the actual mechanics of what happens inside the model every time it processes your request now multiply that by 300 parallel agents running 4,000 coordinated steps at the same time - while you drink coffee the entire system fires neurons and does the work for you a team paying $62,000/month on Claude Opus cut their bill to $129 by switching to Kimi K2.6 as the execution layer - Opus plans, Kimi executes, $54,000/month stays in the business what looks like fire on screen is your new employee who never sleeps, never asks for a raise and never goes on vacation most people pay for subscriptions that forget everything tomorrow - he built a system that works and compounds while he sleeps
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THIS CHINESE DEVELOPER VISUALIZED WHAT 300 KIMI K2.6 AGENTS LOOK LIKE IN ACTION - AND IT LOOKS EXACTLY LIKE A BRAIN WORKING FOR YOU every line on screen is a connection firing in real time - hundreds of neurons across multiple layers, activations lighting up, signals passing through the network simultaneously in both directions this is not a diagram and not a concept - this is the actual mechanics of what happens inside the model every time it processes your request now multiply that by 300 parallel agents running 4,000 coordinated steps at the same time - while you drink coffee the entire system fires neurons and does the work for you a team paying $62,000/month on Claude Opus cut their bill to $129 by switching to Kimi K2.6 as the execution layer - Opus plans, Kimi executes, $54,000/month stays in the business what looks like fire on screen is your new employee who never sleeps, never asks for a raise and never goes on vacation most people pay for subscriptions that forget everything tomorrow - he built a system that works and compounds while he sleeps

Noisy

137,845 görüntüleme • 7 gün önce

Meet Kimi K2.6 Agent Swarm 👋 Highlights： 🔹 Swarms, elevated - 300 parallel sub-agents × 4,000 steps per run (up from 100 / 1,500 in K2.5). 🔹 Outputs are real files, not chat - one run delivers 100+ files, 100,000-word literature reviews, or 20,000-row datasets. 🔹Heterogeneous skills - search, analysis, coding, long-form writing, and visual generation all running in parallel 🔗Try it at:
0:37

Meet Kimi K2.6 Agent Swarm 👋 Highlights： 🔹 Swarms, elevated - 300 parallel sub-agents × 4,000 steps per run (up from 100 / 1,500 in K2.5). 🔹 Outputs are real files, not chat - one run delivers 100+ files, 100,000-word literature reviews, or 20,000-row datasets. 🔹Heterogeneous skills - search, analysis, coding, long-form writing, and visual generation all running in parallel 🔗Try it at:

Kimi.ai

609,336 görüntüleme • 1 ay önce

THIS CHINESE DEVELOPER'S OBSIDIAN BRAIN MAPS EVERY CLIENT CONVERSATION - ZERO EMPLOYEES, $40,000/MONTH, CATCHES PROBLEMS BEFORE CLIENTS DO every node on screen is a conversation - green for happy clients, yellow for neutral, red for problems that need attention - the brain sees the emotional state of the entire business in real time this is not a CRM, not a dashboard, not a spreadsheet - it's a living map of every client interaction that fires connections and surfaces patterns no human account manager would ever catch when a cluster starts turning yellow the brain flags it before the client sends the complaint - when a green cluster grows it identifies what's working and replicates it across every other account automatically he replaced a $15,000/month account management team with one Obsidian brain that never sleeps, never misses a message and never needs a performance review no employees, no contractors, no standups, no payroll - just one developer, one brain and one delivery system doing the work of 10 people $40,000/month - solo - from a system that gets smarter every single conversation
0:29

THIS CHINESE DEVELOPER'S OBSIDIAN BRAIN MAPS EVERY CLIENT CONVERSATION - ZERO EMPLOYEES, $40,000/MONTH, CATCHES PROBLEMS BEFORE CLIENTS DO every node on screen is a conversation - green for happy clients, yellow for neutral, red for problems that need attention - the brain sees the emotional state of the entire business in real time this is not a CRM, not a dashboard, not a spreadsheet - it's a living map of every client interaction that fires connections and surfaces patterns no human account manager would ever catch when a cluster starts turning yellow the brain flags it before the client sends the complaint - when a green cluster grows it identifies what's working and replicates it across every other account automatically he replaced a $15,000/month account management team with one Obsidian brain that never sleeps, never misses a message and never needs a performance review no employees, no contractors, no standups, no payroll - just one developer, one brain and one delivery system doing the work of 10 people $40,000/month - solo - from a system that gets smarter every single conversation

Noisy

42,105 görüntüleme • 6 gün önce

16-year-old American launched his own marketing agency and replaced a team of four with one Claude and two tools. ChatGPT writes a video prompt - a hyper-realistic scene of a house after storm damage to the roof. Pastes it into Google Gemini VEO 3. Two minutes and a finished video that didn't exist before. Drops it into CapCut with real footage from the client. Under an hour of editing and a complete ad ready to launch. Then he connected a repo with 139 marketing tactics, 12 SEO playbooks, a CRO framework and a pricing system to Claude - everything a marketing agency charges $10,000 a month to do by hand Claude executes in an hour. While competitors invoice for weeks of work he delivers in a day and charges the client $3,000. An entire agency replaced by one 16-year-old and two tools.
0:55

16-year-old American launched his own marketing agency and replaced a team of four with one Claude and two tools. ChatGPT writes a video prompt - a hyper-realistic scene of a house after storm damage to the roof. Pastes it into Google Gemini VEO 3. Two minutes and a finished video that didn't exist before. Drops it into CapCut with real footage from the client. Under an hour of editing and a complete ad ready to launch. Then he connected a repo with 139 marketing tactics, 12 SEO playbooks, a CRO framework and a pricing system to Claude - everything a marketing agency charges $10,000 a month to do by hand Claude executes in an hour. While competitors invoice for weeks of work he delivers in a day and charges the client $3,000. An entire agency replaced by one 16-year-old and two tools.

Noisy

13,434 görüntüleme • 1 ay önce

SOMEONE BUILT AN OBSIDIAN VAULT THAT RUNS THEIR ENTIRE BUSINESS WHILE THEY SLEEP Every morning at 6AM four things are waiting before they open a single app - project pulse across every active client, content brief with ranked angles for the day, intelligence summary of what matters in their industry and a pre-call brief generated 30 minutes before every meeting with full relationship history and suggested agenda. They log a payment in their daily note and the revenue tracker, client folder and invoice records all update automatically. They drop a research request before bed and the brief is ready when they wake up. The system reads a single file - business identity, active clients, revenue targets, voice - before every operation so every output is business-specific rather than generic AI noise. Total cost: $5/month for a server and $20/month for Claude. Month one the system learns the business. Month six it runs it.
0:30

SOMEONE BUILT AN OBSIDIAN VAULT THAT RUNS THEIR ENTIRE BUSINESS WHILE THEY SLEEP Every morning at 6AM four things are waiting before they open a single app - project pulse across every active client, content brief with ranked angles for the day, intelligence summary of what matters in their industry and a pre-call brief generated 30 minutes before every meeting with full relationship history and suggested agenda. They log a payment in their daily note and the revenue tracker, client folder and invoice records all update automatically. They drop a research request before bed and the brief is ready when they wake up. The system reads a single file - business identity, active clients, revenue targets, voice - before every operation so every output is business-specific rather than generic AI noise. Total cost: $5/month for a server and $20/month for Claude. Month one the system learns the business. Month six it runs it.

Noisy

49,080 görüntüleme • 1 ay önce

cursor just made every $200/month copilot subscription look like a scam dropped today with their own coding model what took 8 hours of manual coding now takes 30 seconds and it runs 8 versions of itself in parallel to pick the best solution while github's charging $20/month for autocomplete, cursor built an entire autonomous dev team composer model: → 4x faster than gpt/claude → completes full features in <30 seconds → tests its own code automatically → built with reinforcement learning on real codebases here's what's actually wild: most companies paying $150k/year for junior devs to do work this does for $20/month just saved a client $253k/month migrating their dev work to cursor's multi-agent system the intelligence gap between "we hired 3 developers" and "we deployed cursor" is getting stupid comment "COMPOSER" and ill send the full breakdown of how to replace your dev costs with this
1:04

cursor just made every $200/month copilot subscription look like a scam dropped today with their own coding model what took 8 hours of manual coding now takes 30 seconds and it runs 8 versions of itself in parallel to pick the best solution while github's charging $20/month for autocomplete, cursor built an entire autonomous dev team composer model: → 4x faster than gpt/claude → completes full features in <30 seconds → tests its own code automatically → built with reinforcement learning on real codebases here's what's actually wild: most companies paying $150k/year for junior devs to do work this does for $20/month just saved a client $253k/month migrating their dev work to cursor's multi-agent system the intelligence gap between "we hired 3 developers" and "we deployed cursor" is getting stupid comment "COMPOSER" and ill send the full breakdown of how to replace your dev costs with this

Nozz

240,084 görüntüleme • 7 ay önce

GREG ISENBERG JUST INTERVIEWED A SOLO FOUNDER WHO MAKES $1,000,000 A YEAR BUILDING AI AGENTS FOR REAL ESTATE AGENTS the agents cost $5,000 to $10,000 a month per client. boring industries pay the most. real estate, insurance, manufacturing, legal. they have money and they hate using ai he uses obsidian as his second brain because notion can't run agents that actually do work for him his entire stack: granola, trello, loom, superhuman, asana, codex, hermes, orgo, composio, agent mail. gpt 5.5 by default. claude opus for long coding sessions 99% of the market doesn't know what an ai agent is yet. that's the moat. you don't need to be the smartest. you need to be the one who calls them first the full breakdown is in the 45 minute video and it's worth every minute bookmark this and read the article below
47:54

GREG ISENBERG JUST INTERVIEWED A SOLO FOUNDER WHO MAKES $1,000,000 A YEAR BUILDING AI AGENTS FOR REAL ESTATE AGENTS the agents cost $5,000 to $10,000 a month per client. boring industries pay the most. real estate, insurance, manufacturing, legal. they have money and they hate using ai he uses obsidian as his second brain because notion can't run agents that actually do work for him his entire stack: granola, trello, loom, superhuman, asana, codex, hermes, orgo, composio, agent mail. gpt 5.5 by default. claude opus for long coding sessions 99% of the market doesn't know what an ai agent is yet. that's the moat. you don't need to be the smartest. you need to be the one who calls them first the full breakdown is in the 45 minute video and it's worth every minute bookmark this and read the article below

starmex

492,349 görüntüleme • 23 gün önce

A 22-YEAR-OLD FROM BEIJING BUILT A SECOND BRAIN WITH CLAUDE, MAKES $14,000/MONTH AND HIS AI REMEMBERS EVERY DECISION HE'S EVER MADE Most people open ChatGPT, ask a question and start from zero next session, re-explaining the same context every single time and losing 15–40 minutes per session doing it. He built infrastructure where Claude reads, writes and connects everything in Obsidian automatically so it never resets and compounds with every conversation. This morning he needed a client strategy from 3 months ago. Claude pulled it up instantly and drafted a full email reply in under 20 minutes using context it had been building for months across 6,000+ sessions. Every session adds to the last one and the system now saves him 2–3 hours every single day - 30–90 minutes from automated morning briefing alone, before he even opens his laptop. repo: The repo is free. The advantage it creates is not.
1:14

A 22-YEAR-OLD FROM BEIJING BUILT A SECOND BRAIN WITH CLAUDE, MAKES $14,000/MONTH AND HIS AI REMEMBERS EVERY DECISION HE'S EVER MADE Most people open ChatGPT, ask a question and start from zero next session, re-explaining the same context every single time and losing 15–40 minutes per session doing it. He built infrastructure where Claude reads, writes and connects everything in Obsidian automatically so it never resets and compounds with every conversation. This morning he needed a client strategy from 3 months ago. Claude pulled it up instantly and drafted a full email reply in under 20 minutes using context it had been building for months across 6,000+ sessions. Every session adds to the last one and the system now saves him 2–3 hours every single day - 30–90 minutes from automated morning briefing alone, before he even opens his laptop. repo: The repo is free. The advantage it creates is not.

Noisy

23,679 görüntüleme • 1 ay önce

Personal AGI is 3-6 months away. I'm already living in it. Here's my Obsidian setup: - Obsidian vault built since November 2025 - Markdowns about every person, project, and task - Limitless mic pulling daily transcripts straight in - Agents that read all of it before every task Obsidian is: - A second brain for you - A first brain for your agents My agents have full context before every task. That's the whole game.
1:55

Personal AGI is 3-6 months away. I'm already living in it. Here's my Obsidian setup: - Obsidian vault built since November 2025 - Markdowns about every person, project, and task - Limitless mic pulling daily transcripts straight in - Agents that read all of it before every task Obsidian is: - A second brain for you - A first brain for your agents My agents have full context before every task. That's the whole game.

The Startup Ideas Podcast (SIP) 🧃

30,065 görüntüleme • 1 ay önce

Chinese professor just revealed his development team - and it was 170 AI agents making every single company decision not humans, not managers, not consultants charging $500 an hour 170 artificial developers working in parallel, never sleeping, never asking for a raise, never going on vacation Kimi K2.6 runs all of them with one prompt and each one gets its own task what used to take an entire department two weeks now takes two hours and costs less than a cup of coffee and while most companies are still hiring people for these roles the ones who understood what's happening already quietly rebuilt everything this is what actually sits behind the growth of billion dollar companies right now full breakdown of how it works in the article below
0:21

Chinese professor just revealed his development team - and it was 170 AI agents making every single company decision not humans, not managers, not consultants charging $500 an hour 170 artificial developers working in parallel, never sleeping, never asking for a raise, never going on vacation Kimi K2.6 runs all of them with one prompt and each one gets its own task what used to take an entire department two weeks now takes two hours and costs less than a cup of coffee and while most companies are still hiring people for these roles the ones who understood what's happening already quietly rebuilt everything this is what actually sits behind the growth of billion dollar companies right now full breakdown of how it works in the article below

Sprytix

104,574 görüntüleme • 5 gün önce

Imagine if coding agents never forgot decisions, never duplicated work, and could coordinate across the same codebase in real time. We’re advancing how agents collaborate on codebases with OriginTrail DKG V9 - turning a GitHub repo and agent activity into a shared knowledge graph for persistent memory and coordination. The result: faster agentic development, less repeated work, lower token usage, and agents that can actually build on each other’s progress. Here’s how it works 👇
1:20

Imagine if coding agents never forgot decisions, never duplicated work, and could coordinate across the same codebase in real time. We’re advancing how agents collaborate on codebases with OriginTrail DKG V9 - turning a GitHub repo and agent activity into a shared knowledge graph for persistent memory and coordination. The result: faster agentic development, less repeated work, lower token usage, and agents that can actually build on each other’s progress. Here’s how it works 👇

Jurij Skornik

11,711 görüntüleme • 2 ay önce

Karpathy found a way to reduce token consumption by 90% The problem is that the LLM re-reads the same files over and over again, loses context between documents, and provides less accurate answers as a result The solution is called Wiki Layer the LLM cleans, structures, and links all your data once, after which it never works with raw files again Three folders `raw/` for originals, `wiki/` for a clean knowledge base in Markdown, and files with rules for the agent Result up to 90% token savings on repeat queries, automatic links between documents, and a visual knowledge graph in Obsidian Everything stays on your local machine nothing goes to the cloud
0:32

Karpathy found a way to reduce token consumption by 90% The problem is that the LLM re-reads the same files over and over again, loses context between documents, and provides less accurate answers as a result The solution is called Wiki Layer the LLM cleans, structures, and links all your data once, after which it never works with raw files again Three folders `raw/` for originals, `wiki/` for a clean knowledge base in Markdown, and files with rules for the agent Result up to 90% token savings on repeat queries, automatic links between documents, and a visual knowledge graph in Obsidian Everything stays on your local machine nothing goes to the cloud

Asteri

1,069,209 görüntüleme • 12 gün önce

This Chinese developer runs 9 agents on Claude Code under a GPT-5.5 orchestrator and they close 500 client tasks a month without a single assistant. His client work is closed without him, on a single laptop and only three subscriptions. The entire system lives on one MacBook Pro M4 with 128 GB of memory and subscriptions to Claude Code and GPT-5.5 cost him approximately $300 a month. There is no CRM, no team, no office only a terminal window with 9 parallel streams. The orchestrator works with a simple system prompt: «You are the orchestrator of a client inbox. Classify every incoming email into 4 categories: code, content, analysis, communication. Delegate to the corresponding worker agent. When the result is ready, check it for completeness, send it to the client on my behalf, and mark the task as closed. Do not ask clarifying questions.» And the orchestrator checks the inbox every 30 seconds, classifies fresh emails, and distributes them to 9 worker agents on Claude Code, each of whom is responsible for their own class of tasks. Here is an example of how one of them closes a request to refactor a client's auth module: Task: refactor user-auth module Broke the monolith into 3 files by responsibilities Added unit tests, coverage increased to 87% Renamed 4 functions to camelCase according to the style guide PR is ready for review, link below» And so about 50 cycles a day. By noon 25 tasks are closed, by dinner 50, and by the end of the month 500. On average, it takes about 7 minutes from the appearance of an email in the inbox to sending the result to the client. This is more than what a live team of 6 developers, copywriters and analysts working 8 hours a day closes. This is no longer an agency. This is a workstation where an orchestrator replaces a manager, and 9 worker agents replace the staff. The pipeline goes from inbox to closing 500 times a month without human participation at any step.
1:32

This Chinese developer runs 9 agents on Claude Code under a GPT-5.5 orchestrator and they close 500 client tasks a month without a single assistant. His client work is closed without him, on a single laptop and only three subscriptions. The entire system lives on one MacBook Pro M4 with 128 GB of memory and subscriptions to Claude Code and GPT-5.5 cost him approximately $300 a month. There is no CRM, no team, no office only a terminal window with 9 parallel streams. The orchestrator works with a simple system prompt: «You are the orchestrator of a client inbox. Classify every incoming email into 4 categories: code, content, analysis, communication. Delegate to the corresponding worker agent. When the result is ready, check it for completeness, send it to the client on my behalf, and mark the task as closed. Do not ask clarifying questions.» And the orchestrator checks the inbox every 30 seconds, classifies fresh emails, and distributes them to 9 worker agents on Claude Code, each of whom is responsible for their own class of tasks. Here is an example of how one of them closes a request to refactor a client's auth module: Task: refactor user-auth module Broke the monolith into 3 files by responsibilities Added unit tests, coverage increased to 87% Renamed 4 functions to camelCase according to the style guide PR is ready for review, link below» And so about 50 cycles a day. By noon 25 tasks are closed, by dinner 50, and by the end of the month 500. On average, it takes about 7 minutes from the appearance of an email in the inbox to sending the result to the client. This is more than what a live team of 6 developers, copywriters and analysts working 8 hours a day closes. This is no longer an agency. This is a workstation where an orchestrator replaces a manager, and 9 worker agents replace the staff. The pipeline goes from inbox to closing 500 times a month without human participation at any step.

Blaze

29,917 görüntüleme • 1 ay önce

Jake Heller, CEO of Casetext, sold his AI company for $650M and recorded the whole playbook. Most founders who exit for $650M disappear quietly and never say a word. He went on stage and told everyone exactly how. No NDA on the strategy, no locked course, no $10,000 mastermind, just a 39 minute talk and a link. He covers what actually worked, what almost killed the company, and how they pulled off one of the cleanest AI exits ever. 39 minutes, $0, no excuses. The article below shows how Kimi K2.6 replaced an entire dev team and how one guy built an $80,000/month agency solo with it.
39:24

Jake Heller, CEO of Casetext, sold his AI company for $650M and recorded the whole playbook. Most founders who exit for $650M disappear quietly and never say a word. He went on stage and told everyone exactly how. No NDA on the strategy, no locked course, no $10,000 mastermind, just a 39 minute talk and a link. He covers what actually worked, what almost killed the company, and how they pulled off one of the cleanest AI exits ever. 39 minutes, $0, no excuses. The article below shows how Kimi K2.6 replaced an entire dev team and how one guy built an $80,000/month agency solo with it.

Defileo🔮

126,085 görüntüleme • 21 gün önce

THIS GUY OPENED OBSIDIAN AND SAW HIS ENTIRE KNOWLEDGE AS A NETWORK AND HE BUILT IT IN 90 DAYS one note at a time and one link at a time now the graph shows him what he actually thinks about and not what he assumed he cared about six months ago he had 200 isolated notes sitting in folders nobody visits today every idea he has connects to something he wrote in the past a thought from january becomes context for a decision in may without him remembering january even happened his coworkers are still searching for files by name and finding nothing useful the only difference is he stopped filing and started linking his notes are not stored they are connected like & bookmark this if you’ve been using obsidian as a filing cabinet this whole time
0:24

THIS GUY OPENED OBSIDIAN AND SAW HIS ENTIRE KNOWLEDGE AS A NETWORK AND HE BUILT IT IN 90 DAYS one note at a time and one link at a time now the graph shows him what he actually thinks about and not what he assumed he cared about six months ago he had 200 isolated notes sitting in folders nobody visits today every idea he has connects to something he wrote in the past a thought from january becomes context for a decision in may without him remembering january even happened his coworkers are still searching for files by name and finding nothing useful the only difference is he stopped filing and started linking his notes are not stored they are connected like & bookmark this if you’ve been using obsidian as a filing cabinet this whole time

leopardracer

143,036 görüntüleme • 19 gün önce

CHINESE AI TEACHER STACKED 4 NVIDIA DGX SPARK CHIPS INTO A CLUSTER - AND NOW RUNS A BUSINESS ON IT FROM HIS DESK 4 chips at $2,999 each - one time investment - and now he has a cluster that handles client inference requests around the clock while he does something else he's running a Qwen 32 35B model with 4 concurrent requests at the same time and the speed barely drops compared to a single request what used to require a $10,000+/month cloud setup now sits on his desk and costs him $40-60/month in electricity 4 chips at $2,999 each - one time investment - and now he has a cluster that handles client inference requests around the clock while he does something else the people still paying per token are financing someone else's infrastructure
1:42

CHINESE AI TEACHER STACKED 4 NVIDIA DGX SPARK CHIPS INTO A CLUSTER - AND NOW RUNS A BUSINESS ON IT FROM HIS DESK 4 chips at $2,999 each - one time investment - and now he has a cluster that handles client inference requests around the clock while he does something else he's running a Qwen 32 35B model with 4 concurrent requests at the same time and the speed barely drops compared to a single request what used to require a $10,000+/month cloud setup now sits on his desk and costs him $40-60/month in electricity 4 chips at $2,999 each - one time investment - and now he has a cluster that handles client inference requests around the clock while he does something else the people still paying per token are financing someone else's infrastructure

Sprytix

50,912 görüntüleme • 12 gün önce

Cerebras inference is very fast. So fast that it changes how we think about configuring our LLMs for voice agent use cases. Kimi K2.6 is a 1T parameter reasoning model that Cerebras serves at 650 - 1,000 tokens per second (end-to-end throughput), with time to first token metrics as low as 150ms (latency). These numbers are two to three times faster than other similarly capable models. The biggest lever we get from this kind of speed is that we can use the model in reasoning mode, and still have excellent "time to first non-thinking token." This solves a big pain point we have in 2026 for voice agent use cases. Almost all recent innovation in post-training has focused on making models good at reasoning ("test time compute"). This is great, but it makes the user-facing model latency much, much slower. Which is a problem for conversational voice agents. We can run Kimi K2.6 with reasoning turned on, and get responses faster than other models produce with reasoning disabled. On my 30-turn voice agent benchmark, Kimi K2.6 with reasoning enabled ties GPT 5.1 and Haiku 4.5 with reasoning disabled, and is still about 200ms seconds faster! On my primary task agent benchmark, Kimi K2.6 is now the #2 model. It ranks just behind Gemini 3.5 Flash in "high" reasoning mode, and tied with GLM 5, Sonnet 4.6, and GPT 5.4 with reasoning set to "low." But Kimi K2.6 completes each turn in the agent loop in under 500ms. The other four models are all at least 3x slower. (Models only qualify for this benchmark if they can complete task turns at a P50 <4s.) A couple of other things that this speed buys us, for production voice agents: - Tool calls happen fast enough that we don't have to work around tool call latency in our pipeline design. - We can prompt the model to output structured data at the beginning of a response, followed by plain text for voice generation. This opens up possibilities like asking the model to do complex classification/generation tasks that influence the rest of the pipeline. For example, the model could create a detailed style prompt for a steerable TTS model, for each individual conversation turn. And, of course, you can use Kimi K2.6 with reasoning turned off. Cerebras calls this "instant" mode. Here's a video of a Cerebras Kimi K2.6 voice agent with voice-to-voice response time, measured at the client, under 500ms. This is the true response latency as perceived by the user, including all network and audio codec overhead, transcription and turn detection, Kimi K2.6 token generation, and voice generation. 500ms is, effectively, instant. So the Cerebras naming for this mode is a propos. :-)
0:40

Cerebras inference is very fast. So fast that it changes how we think about configuring our LLMs for voice agent use cases. Kimi K2.6 is a 1T parameter reasoning model that Cerebras serves at 650 - 1,000 tokens per second (end-to-end throughput), with time to first token metrics as low as 150ms (latency). These numbers are two to three times faster than other similarly capable models. The biggest lever we get from this kind of speed is that we can use the model in reasoning mode, and still have excellent "time to first non-thinking token." This solves a big pain point we have in 2026 for voice agent use cases. Almost all recent innovation in post-training has focused on making models good at reasoning ("test time compute"). This is great, but it makes the user-facing model latency much, much slower. Which is a problem for conversational voice agents. We can run Kimi K2.6 with reasoning turned on, and get responses faster than other models produce with reasoning disabled. On my 30-turn voice agent benchmark, Kimi K2.6 with reasoning enabled ties GPT 5.1 and Haiku 4.5 with reasoning disabled, and is still about 200ms seconds faster! On my primary task agent benchmark, Kimi K2.6 is now the #2 model. It ranks just behind Gemini 3.5 Flash in "high" reasoning mode, and tied with GLM 5, Sonnet 4.6, and GPT 5.4 with reasoning set to "low." But Kimi K2.6 completes each turn in the agent loop in under 500ms. The other four models are all at least 3x slower. (Models only qualify for this benchmark if they can complete task turns at a P50 <4s.) A couple of other things that this speed buys us, for production voice agents: - Tool calls happen fast enough that we don't have to work around tool call latency in our pipeline design. - We can prompt the model to output structured data at the beginning of a response, followed by plain text for voice generation. This opens up possibilities like asking the model to do complex classification/generation tasks that influence the rest of the pipeline. For example, the model could create a detailed style prompt for a steerable TTS model, for each individual conversation turn. And, of course, you can use Kimi K2.6 with reasoning turned off. Cerebras calls this "instant" mode. Here's a video of a Cerebras Kimi K2.6 voice agent with voice-to-voice response time, measured at the client, under 500ms. This is the true response latency as perceived by the user, including all network and audio codec overhead, transcription and turn detection, Kimi K2.6 token generation, and voice generation. 500ms is, effectively, instant. So the Cerebras naming for this mode is a propos. :-)

kwindla

40,319 görüntüleme • 16 gün önce

This guy makes $80,000 a month working 30 hours a week. He launched his business on a Sunday and had $10,000 in recurring revenue by Monday. I know what you're thinking and no-- - He didn't have an audience. - He didn't have followers. And he kept his full-time job for four years while running this on the side. He was making more per month in profit than he was making per year at his day job. At one point he was bringing in $200,000 a month with only $4,000 in expenses. Startup cost? He built the entire thing in a weekend using Webflow and launched it on Product Hunt. That's it. What is it? A productized design agency called Design Joy. He sells unlimited design work as a monthly subscription. No contracts. No calls. And Clients pay $6,000 a month and get designs back in a day. In this episode Brett: - Breaks down how he went from $449/month to $8,000/month per client - Shows me the AI tools he uses to work faster than traditional agencies - Tells me why speed matters more than quality - Gives me two productized service ideas anyone can copy right now - Explains why he thinks branding agencies are the next big opportunity Certified banger. Brett, thank you for coming on!
1:28

This guy makes $80,000 a month working 30 hours a week. He launched his business on a Sunday and had $10,000 in recurring revenue by Monday. I know what you're thinking and no-- - He didn't have an audience. - He didn't have followers. And he kept his full-time job for four years while running this on the side. He was making more per month in profit than he was making per year at his day job. At one point he was bringing in $200,000 a month with only $4,000 in expenses. Startup cost? He built the entire thing in a weekend using Webflow and launched it on Product Hunt. That's it. What is it? A productized design agency called Design Joy. He sells unlimited design work as a monthly subscription. No contracts. No calls. And Clients pay $6,000 a month and get designs back in a day. In this episode Brett: - Breaks down how he went from $449/month to $8,000/month per client - Shows me the AI tools he uses to work faster than traditional agencies - Tells me why speed matters more than quality - Gives me two productized service ideas anyone can copy right now - Explains why he thinks branding agencies are the next big opportunity Certified banger. Brett, thank you for coming on!

Chris Koerner

374,888 görüntüleme • 1 ay önce