anybody who uses or learns agentic systems, SHOULD READ THIS the install order I run before any new agentic project: 1. PRIVACY: direnv + a real secrets manager install direnv, then plug it into your team's password manager (1Password CLI via op run, doppler, infisical, vault, pick one) what direnv does: loads per-folder environment variables when you cd in, unloads when you cd out. the real move is wiring it into your secrets manager so credentials NEVER live in plain text on disk what this stops: - API keys accidentally committed to git history, the most common AI agent breach pattern in 2026 - credentials leaking from one project into another through your shell history - shared .env files that one teammate quietly backs up to Dropbox - secrets that survive a laptop theft because they were sitting in /Users/you/projects the part nobody mentions: most "my agent got jailbroken" stories actually trace back to one credential the agent had access to that it shouldn't have. scope keys to projects, scope projects to folders, and the blast radius of any single compromise drops dramatically I shipped 2 agents with keys in .env files before switching. the day I plugged direnv into op run I stopped having that whole class of nightmare 2. TOKENS: litellm or portkey as your model proxy one URL that fronts every AI provider (Anthropic, OpenAI, Google, Mistral, local models). all your spend flows through one place what it saves you: - response caching keyed by prompt hash, cuts your bill 30-60% on repeat tasks - automatic fallback on rate limits (Sonnet hits a 429? falls to Opus, then GPT, then your local backup, no broken users) - per-feature and per-user budget caps, block the call before it costs $200 instead of auditing it after - model routing rules, cheap tasks to Haiku, expensive ones to Opus, never the wrong way - PII redaction before requests leave your network, security side benefit the part nobody mentions: every "$4k AI bill" story I've heard ends with "we didn't have a proxy in front." this is where you put guardrails around spend BEFORE the spend happens I built my own router for 2 weeks. it took 20 minutes to replace with litellm. I will be embarrassed about this forever 3. CONTEXT: uv + git commit on every passing eval install uv (the new Python package manager, 10-100x faster than pip+venv, by the Astral team behind ruff). then commit every time an eval suite PASSES, with the model version and pass rate in the commit message what this preserves: - exact dependency set via uv.lock, you always know which packages your agent was using, no nasty surprises from a quiet update - exact prompt + code state, you can reproduce any past run from a single git hash - exact model version paired to exact pass rate, a paper trail when prod breaks weeks later - one-command rollback to a known-working state when a refactor goes sideways - a compliance story, every prompt version tied to a model version in your commit log the security side: when something blows up in prod, you want to say "the prompt was version X, model was Sonnet 4.6.1, last eval pass rate was 94%." not "I think we deployed on Tuesday?" the first is an incident report. the second is a resignation letter I've lost more agents to "I changed 3 prompts in one session and broke something" than to any actual bug 4. VISIBILITY: mitmproxy in front of every LLM call it's basically a wiretap for your agent. install it, point your agent through it, and now you see every conversation your agent has with the model in real time what actually shows up: - every silent retry your SDK sneaks in when a call fails - the full prompt being sent (including any creds you accidentally embedded) - what the model returns BEFORE your code reacts to it - exact token cost per call, per tool, per loop iteration - responses that quietly trigger your code into doing something you didn't intend, this is where prompt injection lives the part nobody talks about: if a website your agent scraped slipped instructions into its data, mitmproxy is how you SEE the moment your agent decides to follow them. without this layer, you're trusting your agent did the right thing, not verifying I shipped 3 agents before adding this. I have no honest idea what they were doing in production 5. EVALS: inspect-ai (the framework the labs actually use) an eval framework is what tells you "this agent works" with numbers instead of vibes. inspect-ai is the one Anthropic, DeepMind, and the UK AI Safety Institute use for the eval reports you read in their papers. open source, MIT licensed what your homegrown version won't have: - run the same task across 5 different models and compare scores side by side - pre-built tests for risky agent behavior (lying, manipulating, misusing tools) - proper structure for evaluating tool-using agents, not just chat - repeatable scoring, the same input always gets graded the same way - reproducible eval seeds, so a flaky test is actually flaky and not just unlucky I wrote my own eval harness 4 times across 4 projects. threw it out 4 times if you ever want to say "my agent passes safety checks" out loud, the check has to come from a framework someone else can re-run. this is that framework the move that ties this together: keep a /lessons.md in every repo. every weird agent behavior, every edge case, every config change you find at 2am, write it down you will not remember it. you'll come back in 3 weeks and the lessons file is the only reason you still know what's going on lock these 5, keep the lessons file, your next agentic system takes 2 days instead of 2 months p.s. half of "AI agent" content online is people who've never run mitmproxy on their own loop. they don't actually know what their agent is doing. they're shipping demo videos. don't be that guy

anybody who uses or learns agentic systems, SHOULD READ THIS the install order I run before any new agentic project: 1. PRIVACY: direnv + a real secrets manager install direnv, then plug it into your team's password manager (1Password CLI via op run, doppler, infisical, vault, pick one) what direnv does: loads per-folder environment variables when you cd in, unloads when you cd out. the real move is wiring it into your secrets manager so credentials NEVER live in plain text on disk what this stops: - API keys accidentally committed to git history, the most common AI agent breach pattern in 2026 - credentials leaking from one project into another through your shell history - shared .env files that one teammate quietly backs up to Dropbox - secrets that survive a laptop theft because they were sitting in /Users/you/projects the part nobody mentions: most "my agent got jailbroken" stories actually trace back to one credential the agent had access to that it shouldn't have. scope keys to projects, scope projects to folders, and the blast radius of any single compromise drops dramatically I shipped 2 agents with keys in .env files before switching. the day I plugged direnv into op run I stopped having that whole class of nightmare 2. TOKENS: litellm or portkey as your model proxy one URL that fronts every AI provider (Anthropic, OpenAI, Google, Mistral, local models). all your spend flows through one place what it saves you: - response caching keyed by prompt hash, cuts your bill 30-60% on repeat tasks - automatic fallback on rate limits (Sonnet hits a 429? falls to Opus, then GPT, then your local backup, no broken users) - per-feature and per-user budget caps, block the call before it costs $200 instead of auditing it after - model routing rules, cheap tasks to Haiku, expensive ones to Opus, never the wrong way - PII redaction before requests leave your network, security side benefit the part nobody mentions: every "$4k AI bill" story I've heard ends with "we didn't have a proxy in front." this is where you put guardrails around spend BEFORE the spend happens I built my own router for 2 weeks. it took 20 minutes to replace with litellm. I will be embarrassed about this forever 3. CONTEXT: uv + git commit on every passing eval install uv (the new Python package manager, 10-100x faster than pip+venv, by the Astral team behind ruff). then commit every time an eval suite PASSES, with the model version and pass rate in the commit message what this preserves: - exact dependency set via uv.lock, you always know which packages your agent was using, no nasty surprises from a quiet update - exact prompt + code state, you can reproduce any past run from a single git hash - exact model version paired to exact pass rate, a paper trail when prod breaks weeks later - one-command rollback to a known-working state when a refactor goes sideways - a compliance story, every prompt version tied to a model version in your commit log the security side: when something blows up in prod, you want to say "the prompt was version X, model was Sonnet 4.6.1, last eval pass rate was 94%." not "I think we deployed on Tuesday?" the first is an incident report. the second is a resignation letter I've lost more agents to "I changed 3 prompts in one session and broke something" than to any actual bug 4. VISIBILITY: mitmproxy in front of every LLM call it's basically a wiretap for your agent. install it, point your agent through it, and now you see every conversation your agent has with the model in real time what actually shows up: - every silent retry your SDK sneaks in when a call fails - the full prompt being sent (including any creds you accidentally embedded) - what the model returns BEFORE your code reacts to it - exact token cost per call, per tool, per loop iteration - responses that quietly trigger your code into doing something you didn't intend, this is where prompt injection lives the part nobody talks about: if a website your agent scraped slipped instructions into its data, mitmproxy is how you SEE the moment your agent decides to follow them. without this layer, you're trusting your agent did the right thing, not verifying I shipped 3 agents before adding this. I have no honest idea what they were doing in production 5. EVALS: inspect-ai (the framework the labs actually use) an eval framework is what tells you "this agent works" with numbers instead of vibes. inspect-ai is the one Anthropic, DeepMind, and the UK AI Safety Institute use for the eval reports you read in their papers. open source, MIT licensed what your homegrown version won't have: - run the same task across 5 different models and compare scores side by side - pre-built tests for risky agent behavior (lying, manipulating, misusing tools) - proper structure for evaluating tool-using agents, not just chat - repeatable scoring, the same input always gets graded the same way - reproducible eval seeds, so a flaky test is actually flaky and not just unlucky I wrote my own eval harness 4 times across 4 projects. threw it out 4 times if you ever want to say "my agent passes safety checks" out loud, the check has to come from a framework someone else can re-run. this is that framework the move that ties this together: keep a /lessons.md in every repo. every weird agent behavior, every edge case, every config change you find at 2am, write it down you will not remember it. you'll come back in 3 weeks and the lessons file is the only reason you still know what's going on lock these 5, keep the lessons file, your next agentic system takes 2 days instead of 2 months p.s. half of "AI agent" content online is people who've never run mitmproxy on their own loop. they don't actually know what their agent is doing. they're shipping demo videos. don't be that guy

Mythos just lost its preview label on Google Cloud (HACKERS ERA) DISCLAIMER: that means coming days we probably could see the release of the most dangerous model for humanity which literally cracked MacOS for 5 days WHY THIS MATTERS: - Every recent Anthropic model that hit Vertex AI followed the same path: preview label, label removed, public blog post. Opus 4.7 ran this *exact sequence* in the days right before its public release - The preview → GA transition on Vertex AI isn't a UI change. It's a *gated release stage* that requires sign-off from product and security teams on both Anthropic and Google. The label coming off means the model has cleared production-grade review - Vertex AI is the enterprise rail. Once a model is GA there, every Fortune 500 with a GCP contract can hit it from day zero. Faster distribution than the Anthropic console itself - The price per token on Vertex AI is set by Anthropic, not Google. Once the public price drops, the cost of red-teaming this model falls dramatically. Work that was previously limited to a handful of labs becomes accessible to *any developer with a credit card* - This isn't a leak. It's a coordinated launch sequence between two of the largest cloud platforms in the world, and *we're watching step one*

SOMEONE JUST KILLED THE BILLBOARD INDUSTRY a startup ran two ads with the same $5,000 budget on the same day one on Canada's most famous billboard (Yonge-Dundas Square in Toronto, the closest thing Canada has to Times Square) one through 1,970 creators on the internet billboard: 24,311 views creators: 64,562,883 same money, same message, 2,655x gap [ the numbers are insane ]: - billboard views: 24,311 - creator views: 64,562,883 - billboard QR scans: 62 - creator clicks: 11,000+ - billboard CPM: $205.66 - creator CPM: $0.06 - billboard cost per click: $80.65 - creator cost per click: $0.45 [ the mechanism is wild too ]: a billboard makes one bet, it sits 50 feet above a sidewalk and hopes someone looks up an algorithm makes a million bets, targeting this specific person, in this specific minute, scrolling in this specific emotional state, then makes another bet 5 seconds later if it's wrong 1,970 creators dropped the brand's logo into content people already chose to consume: - gaming clips - street interviews - car edits - football edits - relationship drama - meme accounts the logo sat inside videos people share, save, and rewatch instead of one giant rectangle on a wall [ the grift opportunity is even wilder ]: agencies are still charging brands $5,000 for the same billboard slot the same $5,000 routed through creators just did 64M views and 11,000 clicks every CMO still buying TV slots, radio spots, and outdoor billboards in 2026 is leaving 99% of the same budget's reach on the table Content Rewards is 4 months old, already paid $13M+ to creators, and processed 104,785 individual payouts in April alone LINK: THIS IS THE 2018 FACEBOOK ADS WINDOW ALL OVER AGAIN

SOMEONE JUST KILLED THE BILLBOARD INDUSTRY a startup ran two ads with the same $5,000 budget on the same day one on Canada's most famous billboard (Yonge-Dundas Square in Toronto, the closest thing Canada has to Times Square) one through 1,970 creators on the internet billboard: 24,311 views creators: 64,562,883 same money, same message, 2,655x gap [ the numbers are insane ]: - billboard views: 24,311 - creator views: 64,562,883 - billboard QR scans: 62 - creator clicks: 11,000+ - billboard CPM: $205.66 - creator CPM: $0.06 - billboard cost per click: $80.65 - creator cost per click: $0.45 [ the mechanism is wild too ]: a billboard makes one bet, it sits 50 feet above a sidewalk and hopes someone looks up an algorithm makes a million bets, targeting this specific person, in this specific minute, scrolling in this specific emotional state, then makes another bet 5 seconds later if it's wrong 1,970 creators dropped the brand's logo into content people already chose to consume: - gaming clips - street interviews - car edits - football edits - relationship drama - meme accounts the logo sat inside videos people share, save, and rewatch instead of one giant rectangle on a wall [ the grift opportunity is even wilder ]: agencies are still charging brands $5,000 for the same billboard slot the same $5,000 routed through creators just did 64M views and 11,000 clicks every CMO still buying TV slots, radio spots, and outdoor billboards in 2026 is leaving 99% of the same budget's reach on the table Content Rewards is 4 months old, already paid $13M+ to creators, and processed 104,785 individual payouts in April alone LINK: THIS IS THE 2018 FACEBOOK ADS WINDOW ALL OVER AGAIN

Sam Altman literally dropped the playbook for turning an idea into a multi-billion company This 46-minute lecture is the best thing I've watched on raising in 2026 It will help you understand how the early-stage game actually works and which side of the table you're really sitting on Sam ran YC from 2014 to 2019, scaled it into the most well-known accelerator on earth, then left to run OpenAI. He watched thousands of startups fail at every stage and he knows exactly what kills them The part most founders won't want to hear: > 90% of "fundraising advice" online is from people who never raised > the partner picking up your slack DM at 2am matters more than the brand on your cap table > "I raised at a $20M cap" is not a milestone, it's a debt to your next round > half the "top 30 accelerators" lists in your feed still quote 2022 numbers > the right accelerator decides your next 5 years more than the right investor right now the average founder picks an accelerator from a Medium article written 4 years ago.. applies to a program that quietly died.. loses 60 days waiting for a reply that's never coming they think they're doing the work they're using maybe 10% of what's actually available I went through 35 of them this week, verified every check size, every equity %, killed the ones that don't exist anymore Full corrected spreadsheet in the post below

All the best startup accelerators to apply in 2026 (below FULL spreadsheet): [ Top-Tier ] 1. Y Combinator (~$500k, 7% on $125k + uncapped SAFE) 2. a16z Speedrun (~$500k for 10% + $500k follow-on) 3. Techstars (~$220k, bumped from $120k in fall 2025) 4. Founders Inc (~$100-250k for 4-7%) 5. Sequoia Arc (~$1M, terms per company) 6. South Park Commons (~$1M total, $400k for 7% + $600k guaranteed) 7. HF0 (up to $1M uncapped for 5%, repeat founders only) 8. On Deck ODX (DISCONTINUED 2022, skip) 9. Pear VC PearX (~$250k-2M for ~10%) 10. 500 Global Flagship (~$150k for 6%) [ AI / ML Specific ] 11. AI Grant (~$250k for 7%, Nat Friedman + Daniel Gross) 12. AI Fund (~$1M+, Andrew Ng, studio model) 13. NVIDIA Inception (credits + perks, no equity) 14. Microsoft for Startups (up to $150k Azure credits, no equity) 15. Google for Startups AI Accelerator (credits, no equity) [ Vertical / Deep Tech ] 16. SOSV (~$525k across IndieBio, HAX, Orbit) 17. IndieBio (biotech, ~$525k = $250k for 6-8% + Genesis SAFE) 18. HAX (hardware, ~$525k via SOSV) 19. Greentown Labs (cleantech, workspace + grants) 20. Activate (deep science fellowship, 2-yr stipend) [ Crypto / Web3 ] 21. a16z crypto CSX (~$500k for 7%, SF in-person) 22. Alliance (~$500k, ALL18 starts Sept 7) 23. Outlier Ventures Base Camp (~$250k, per-chain verticals) 24. Coinbase Base Builder (varies, mostly non-dilutive) [ International / Regional ] 25. Seedcamp (~€350k-1M first check, rolling, Europe) 26. Entrepreneur First (~$250k = $125k for 8% + uncapped MFN) 27. Antler Disrupt US (~$400k = $250k for ~9% + uncapped) 28. Brinc (~$100k, Asia + Middle East) 29. Station F (Paris campus, hosted-program terms) 30. Founder Institute ($499-999 fee + 2.5% Equity Collective warrant) [ Pre-Seed / Idea Stage / Niche ] 31. Z Fellows (~$10k for 1%, pre-product) 32. ERA NYC (~$100k for 8%, generalist) 33. The Residency (community-first, no standard check) 34. Plug and Play (varies, often non-dilutive) 35. Build For Tomorrow (community + grants) Four things worth knowing: 1. small program acceptance rates are higher than YC, not because they're easier, because the funnel is smaller. apply to 5-7, not 1 2. brand premium on YC is real but not infinite. Arc + Speedrun + HF0 carry signal too 3. "$X for Y%" is the only number that matters. uncapped MFNs are not free money, they dilute you on the next round 4. avoid any list still quoting Techstars at $120k, ODX as active, HF0 at $100k, or Founder Institute as "$10k for 4%". all wrong Shared with you those where I am going to apply with my ideas and products Terms shift annually. Verify on each program's site before applying gl with successful raising

Hermes Agent vs OpenClaw using Qwen 35B Local Model We asked agents to scrape GitHub star history for both tools, find what caused the growth spikes, build a live dashboard in the browser. MacBook Pro M5 Max 64Gb OpenClaw: 203k tokens, 12m 01s - wrote a bash script Hermes: 257k tokens, 33m 01s - wrote a SKILL.md OpenClaw hit GitHub API, got truncated responses, paginated through contributors, pulled star-history JSON, found a security incident in OpenClaw's history, fetched SVGs, fixed broken HTML from trimming, rewrote it clean. Hermes parallel tool calls across GitHub API, web search, and browser. Hit Google rate limit, auto-switched to DuckDuckGo. Fetched article contents, mapped viral moments, then built the dashboard. Both shipped a live dashboard with star growth charts and spike annotations

Yesterday I posted my observations on the new X algorithm in a community-explainer style The post got read literally. Every claim treated as a verified technical fact That wasn't the intent, but it landed that way, and the responsibility for that is mine I deleted the post A more rigorous version is coming where I walk through the actual code line by line and show what genuinely impacts reach The previous version wasn't engagement farming or audience-misleading. It was an attempt to translate technical code into plain English The lesson: I framed observations as if they were verified mechanisms. Next post separates the two cleanly @nikitabier and the X team are doing real work on this platform The current algorithm is fair, and I'm confident it gets better from here Next post on this topic ships with the receipts

a business idea you can start today use Higgsfield Supercomputer to build a faceless content factory for local businesses, creators, or niche media pages the workflow is simple: 1. pick one niche fitness coaches, real estate agents, SaaS founders, e-com brands, restaurants, whatever 2. feed Supercomputer the best-performing videos from that niche it researches what's trending, studies the hooks, pacing, thumbnails, voice, structure 3. make it generate 30-50 variations scripts, voiceovers, visuals, edits, thumbnails, posting angles 4. package this as "done-for-you content engine" not "AI video generation", businesses don't care about that they care about getting content that looks native, ships daily, and doesn't require hiring a full creative team 5. charge $1,000-3,000/month per client one tool does what used to take a scriptwriter, editor, thumbnail designer, VA, and social media manager this is the whole point, AI content tools were cool but the margin is in orchestration one person can run 5-10 clients with this probably one of the easiest agency plays of 2026

How I actually route between models : Tweet drafts : Sonnet 4.6 Long-form articles : Opus 4.6 Code work : Kimi 2.6 Agentic loops : Kimi 2.6 KOL research : Grok 4.3 Quick facts : Perplexity Pro Image gen : GPT-Image-2 Voice consistency : Sonnet 4.6 Boilerplate : Qwen 3 local Yes, single-model setups are why your AI bill is 10x mine

How I actually route between models : Tweet drafts : Sonnet 4.6 Long-form articles : Opus 4.6 Code work : Kimi 2.6 Agentic loops : Kimi 2.6 KOL research : Grok 4.3 Quick facts : Perplexity Pro Image gen : GPT-Image-2 Voice consistency : Sonnet 4.6 Boilerplate : Qwen 3 local Yes, single-model setups are why your AI bill is 10x mine

GOOGLE JUST SHOWED HOW TO BUILD A PRODUCTION-GRADE FINANCIAL ANALYST IN UNDER 10 MINUTES they dropped a full tutorial using Gemini 3 inside Vertex AI Studio and the workflow is insane. - type a slash command like /build - AI generates the code, the API keys and creates the app instantly - handles the data, the diagrams and the evaluation automatically - zero manual setup, zero configuration the whole thing is vibe coding at a production level. no boilerplate, no infrastructure headaches, just prompt and ship.

Met a guy making $3.6 million a year With one of the cleanest digital plays I've ever heard of He scrapes the App Store for apps with 50k+ downloads but no updates in 18+ months DMs the developers with $2-3k cash offers. Most accept fast, they've moved on Hires a Filipino dev for $400/month to add subscriptions and refresh the listing Each app starts pulling $800-3,000/month passive Owns 220+ across categories he doesn't even use Spends 5 hours a week on the whole portfolio Inspiring

startup idea for you use OpenHands (75k+ github stars) + 131 free subagents to sell a "done-for-you AI engineering team" to SMBs that need custom software what's OpenHands? open-source agent runtime that runs Claude/GPT as actual software engineers. they read your codebase, edit files, run tests, open PRs idea: 1. self-host OpenHands on a $20/mo VPS. claude code walks you through setup in an afternoon 2. pick one niche. real estate brokerages, dental practices, law firms, marketing agencies. SMBs that need custom software but can't afford a developer 3. wrap it in their language. "your AI engineering team for dental practices" not "an OpenHands instance with 131 subagents." 4. install the relevant subagents from VoltAgent. crm-specialist for real estate, hipaa-auditor for healthcare, document-automation for law firms 5. plug in MCP servers from the 14k+ available. GitHub, Stripe, Twilio, Postgres, Calendly. now your AI team can ship, deploy, integrate, and notify 6. charge $2,000-5,000/month per client. nothing compared to a $15k/mo dev shop or a $25k/mo junior hire. you're 5x cheaper and the work doesn't stop overnight 7. build one landing page. one onboarding call. record the AGENTS.md setup once. the rest is supervision 8. become the "AI engineering team for [niche]" person on X, LinkedIn, YouTube. share what your agents shipped this week. case studies sell themselves 9. reinvest profits into vertical-specific agents. a "patient-intake-automator" for dental. a "lease-document-generator" for real estate. now you own the vertical these businesses KNOW they need custom software. they hate hiring developers. they will never find OpenHands on github. they will google "outsource my software development." that's you open source is the new wholesale. the code is free. the orchestration is where the margin lives one person can do this. two-person team scales to $50k/mo. you don't need funding. you don't need an office. you need a laptop, a niche, and the willingness to start someone is going to do this. might as well be you p.s. repo into the article below

Anthropic's Claude team just showed the real fix to a $4,200/month AI coding bill 15-minutes. free. by the people who built Claude one person + Skills + smart routing = the same shipping speed at 7% of the cost worth more than any $500 vibe-coding course Bookmark & watch today

Jane Street pays $750k/year for quants who can run neural networks across thousands of market signals This 1-hour Cornell lecture by Marcos Lopez de Prado gives you the same framework those quants get paid $60k/month for Bookmark & watch today. Then read the article below to repeat

GTA 6 isn't out yet and it's already a better business school than YC: 1. Take 10 years to ship your MVP. release date is whenever nobody cared GTA 6 was 12 years coming. 200M+ pre-orders already. ship when it's right, not when investors push you 2. Burn $2 billion on development. don't apologize the cheapest cofounder in the room is always the most expensive in the cap table. fund the vision or ship a knockoff 3. One trailer broke YouTube hype isn't marketing. hype is patience compounded into a single 90-second drop 4. Two protagonists > one solo founder is for content creators. real companies have a Jason AND a Lucia 5. Online mode is where the real money lives GTA V cost $60. GTA Online printed $8 billion in DLC. your "core product" is a loss leader for the recurring rev underneath it 6. Miami is your TAM. don't argue with the map trying to expand to liberty city in your seed round is how Series A's die 7. The hype before launch IS the launch GTA 6 isn't out and it already won 2026. nobody plays a game with no waitlist. nobody buys a tool with no hype. build the trailer before you build the product. 8. You only get a few shots. ship the kind of thing other people copy for 12 years GTA V shaped the industry for over a decade. if your roadmap doesn't have a 12-year industry-shaping move, you're building the wrong thing study this.