I spent hours analyzing X's new open-source algorithm. Here's exactly how the "For You" feed decides what you see - and how to write "bangers": 🧵

Neurobro is dangerously addictive right now! As of today our team gets ~95% of its financial research directly from Neurobro from both web & mobile Portfolio movements, market news, Tinder-style news swiping & deep portfolio analysis All synced in one place And we’re also building a massive global regional leaderboard where everyone will be able to compete, prove their edge & climb the ranks!! Stocks. Crypto. Forex. XAU. Day trading. Long-term investing. There’s something for everyone! If you use Neurobro -- you’re GMI 🔥

POV: me waiting in the queue on my $200/mo Claude Code Max subscription

🚨BIG Daily Update Neurobros! Today, we officially launched the Neurobro API👀 For months, we’ve been getting dozens of requests every single week from individuals & startups asking for direct access to Neurobro intelligence through APIs And now - it’s finally here! Here’s what NeuroAPI offers: • Public paid API access to Neurobro’s financial intelligence AI • The same intelligence powering the Neurobro Mobile App & Neurodex • Low latency & high throughput infrastructure • Ability to build market research tools, dashboards, chatbots & AI agents without needing to build the entire reasoning/data infrastructure yourself • Coverage across crypto, stocks, forex, commodities & prediction markets This is a massive step for the Neurobro ecosystem & opens the doors for developers companies globally to build directly on top of our intelligence layer Go test it out - you can find the link below👇

Why opus 4.6 became so stupid recently?! It's kinda insane that now it introduces more weird edge cases then fixes...

If you are applying in 2026, this is why the market feels fake: I'm probably not supposed to post this publicly... After spending weeks hiring at Axioma AI and Neurobro, studying candidates, researching companies & seeing how this game is actually played -- a lot of it really is fake Not all of it, but enough to make smart people feel insane My view on why market feels so broken: 1. Bad communication → for whatever reason ghosting after 4 interview rounds is a new "normal" 2. AI rejects people before humans even see them. This is the saddest truth. Even smallest family-ran companies use AI screeners to "auto-kill" the best candidates out there 3. Ghost jobs → 25-30% of all companies post roles they have no intention of filling -- just to hit kpis and/or while they already know who takes the job 4. Real well-paid jobs go to referrals, warm intros & internal candidates first in 99% of cases 5. Reposting jobs → i recently spoke to a guy in Serbia who kept reposting the same 3 roles for months even after hiring the people he needed to create "impression of expansion". Absolutely disgusting illusion of opportunity 6. AI created an ugly loop on both sides. You send your "perfect" AI-written CV, company's agents perfectly reject your AI-written CV → kinda an infinite loop of garbage rotation 7. Seniors taking mid-level jobs → Tens of thousands of laid-off seniors apply for junior/mid jobs which makes points 1-6 even worth. Like a cherry on top. Juniors are now competing with people who already shipped real products, led teams, survived 5 layoffs & are willing to take a title cut just to stay in the game Tbh, I do not think the market gets fixed until more people say the quiet part out loud that current hiring is: - too performative - too automated on both ends - about looking "busy" and "growing" - deeply broken Do not let a broken system convince you that you are the problem. Stay strong! » Vlad

If you are looking for a job in 2026, understand this before anything else: I have been on both sides of this in recent years - hiring & searching for a job You are not competing with beginners anymore. You are competing with smart, hungry & overqualified people who are applying to the exact same “entry-level” roles as you And imo, this is the only playbook that really matters now: 1. Get any real experience you can brutally fast → any unpaid hardcore internship beats courses 2. Stop assuming entry position is easy. No it's not. There are a lot of exceptionally smart overqualified people you compete with 3. Build only 3 strong projects - not 20 toy ones. Learn how to scale, deploy, market, sell, etc. 4. If there is a company you really want -- attack from multiple angles → networking, cold outreach, public events, mutuals, etc. 5. Network directly → recruiters, warm contacts, founders, managers matter MUCH more now 6. Broaden the first role. Look for adjacent roles in the same field to get in 7. Boost soft skills. In the AI era people who can think clearly, speak clearly & make others trust - win 8. Be better than "average". Market is brutal so "good enough” isn't enough anymore 9. Accept volume is part of the game. In 2026 sending 200-300 applications is not "crazy" anymore. Unfortunately it is a new "normal" While market is brutal, it's not impossible. And if it's not impossible -- you can do it! Good luck out there folks!

Why are entry level engineering roles so hard to get right now? Recently I talked about that junior devs are cooked. Now let me elaborate on it... AI in this equation is not the only reason. But it has entirely changed hiring incentives. Right now a few things are happening at the same time. I. Companies are under pressure Markets are slower, budgets are tighter, hiring is much more cautious (especially in Europe where labor protections make companies more careful with new hires) II. AI boosts senior productivity One experienced 10x engineer with good AI tooling/ecosystem around him can now do work that previously required a small team From a company perspective prompting AI is in 99% cases cheaper than onboarding, mentoring & managing multiple junior engineers III. AI changes the junior learning path Before juniors learned by doing small tasks. Now many of those tasks are done by AI → traditional learning ladder is disappearing → the concept of learning is being redefined Many teams now prefer one strong engineer AI instead of several junior roles. We see it ourselves at @axioma_ai that competition for 10x devs skyrocketted in the last few months With that said, I believe that learning data engineering, SQL or in general software engineering is NOT useless Far from it. I believe it raised the bar for how quickly you can apply them. For juniors knowing the fundamentals is no longer enough on its own You are now expected to: 1. Use AI to read & understand unfamiliar code (a lot of it) 2. Iterate on godlike speeds 3. Debug & reason about systems much more efficiently 4. Build actually working prototypes/products in days do 100 other small things with AI "in background" This is the state of the market now. And it will only get more brutal each month from now on!

The hardest bugs aren't in your code They're in the space between your code and the real world There are bugs that only appear when thousands of users from all around start using your app From small islands in Indonesia 🇮🇩 to remote areas in USA 🇺🇸 The kind of bug where everything works perfectly on your device, works in staging, works in testing, works for 98% of your users And there's other 2% that somehow always find the thing that breaks. To put in perspective, 2% of users is around ~3600 users... which is A LOT! I spent 15 hours: 1. going back and forth with Claude Code 2. pulling traces 3. cross-referencing device logs 4. checking transport layer behavior 5. reading source code like there's no tomorrow AI is an incredible thinking partner, but some issues require much bigger understanding than just the source code AI doesn't feel the frustration of "it works on my machine" It doesn't have the context of why THIS user on THIS network sees the error and no one else does TLDR → The root cause turned out to be so fundamental that once you see it, you can't unsee it -- a one-line conceptual fix related to how cellular networks behave under certain conditions Was hell of a ride The toughest debugging still requires a human who cares enough about the code to keep digging when the easy answers don't work AI makes you 10x faster at exploring hypotheses but at the end you still have to know which hypotheses to explore

Not a single AI agent beats the creativity you can express on paper There is still something weirdly powerful about a blank sheet of paper with no autocomplete, no next token prediction & prompt engineering Brain pen paper I feel like writing forces an absolute different mode of thinking, which is usually: - much slower - messier - much more honest It's chaos & imperfection & mess that no model can reproduce yet. Something deeply personal & painfully authentic

Chat in ChatGPT is dead gpt-5.4 the "sota" model became absolutely unusable to me 1. hallucinations worse than gpt-5 (lmao) 2. default writing style is THE WORST 3. too many assumptions instead of clarifying 4. absolutely not listening to instructions What happened @OpenAI ?

AI agents are "useless" That's what a friend of mine said recently after trying ChatGPT like 2 years ago. For those who're not in the bubble like i am -- 2 years ago is a prehistoric time in AI. In 2023 models were basically like polite autocomplete. I asked like what do u mean? What did u use it for? He answered: as a teacher i tried to summarize school notes, do some research on topic XYZ and prepare some stupid report. At every single stage ChatGPT "sucked" he said. Well, of course it sucked. Today all those models are different species. The problem is humans treat AI like a microwave. From my observations it usually looks like this: 1. You try once to warm up food 2. Food is cold 3. You declare the whole concept of microwaves is dead forever ☠️ The AI is more like a kitchen -- it changes daily -- sometimes multiple times a day. With some back and forth my answer to him was: If you still think agents are useless, you're not entirely wrong but you're running year 2023 in 2026 world There's no massive learning here. Just go update your mental model cause it's 2026 out there » Vlad

I'm sorry for all junior positions Any top tier AI agent's "annual" salary is ~$2,400 -- and it's getting cheaper every single month It completes tasks super fast, works 24/7, (usually) much more reliable with no hidden costs whatsoever All models score exceptionally well on SWE benchmarks (actually more than enough to outperform any junior) And in addition to that -- no sick leaves with zero stress Junior devs, I'm really sorry for you. It is really tough out there Stay strong!! » Vlad

It's not latency - it's retention

Me every time Claude does something stupid

Don't forget to our tomorrow's AMA! A lot of things were neurcooked recently & much more to come 👀

He who fights AI should make sure he isn't replaced in 1-2 years One week. This is how long your ideas & assumptions hold. I always catch myself in "no way AI can do that" to catch myself 2 weeks later "goddamn it very well can" One week is the current span of what's up-to-date. The best & worst part -- it happens every single week -- sometimes multiple times a week -- on spicy days even multiple times per hour (hello @AnthropicAI & @OpenAI) AI agents aren't new. But it's fascinatingly scary how recent advances in agentic coding started spreading way beyond writing software. Plague-wise AI agents got access to our emails, chats, calendars & moodboards. - Claude Code - Claude Cowork - Codex - Gemini CLI - OpenClaw - ... This list goes on and on. The jump in how much models can do on their own in the last 4 months was so brutal, it’s hard to overstate. But hey, this is me grinding non-stop on @0xNeurobro & @axioma_ai where it's literally my job as a CAIO to know all the latest research/tools & sit where the edge is sharpest. So what do the rest of us do? Stop trying to “catch up” The biggest issue we were taught in the last 2-3 years - "learn what AI can do" This approach is breaking down faster than your relevance. The word "learn" is the biggest problem. Learning implies stability, some body of knowledge you can absorb, maybe master and then move on. In 2026 by the time you learn something AI-related, ground underneath shakes so hard that your knowledge body is already either: - a SKILL.md for someone's agent - is someone's SaaS - is a button in some package Thousands other things i want to say in this mini-essay, but I'll compress my learned takeaways as hard as i can. I. Update daily your mental model To stay relevant, one must update the mental model from "AI is a tool i have to learn" to "AI is a multiplier i use by default". Everywhere. I mean every-where. This is in my opinion the hardest part. II. Remove limitations Any human working with a computer has to get rid of those fomulations: - i can't do... - i have no experience in... - if only I had... If u work with any digital device - there's no way u can't do stuff. Period. There's a sweet spot most AI folks end up in -- found only through hands-on work -- which is somewhere between Übermensch & absolute clown. Hard to describe unless point I. clicks for you. III. Feel uncomfortable The most relevant people i know are all a bit anxious. If u feel calm, you're probably not close enough to the edge with experimentations. If you don’t feel at least mildly threatened by what models can now do autonomously, you’re not looking closely enough. IV. Stay on @X With all its downsides, Twitter is still #1 place for all technical discussions, updates, releases & people who you can ask in replies to build stuff and one week later they actually ship it (happened to me twice already) Wish u good luck, dear reader, in those biblical AI times. Let me know if i can help! » Vlad

Building for tens of thousands of users is a different sport. We recently hit rank #89 in the “Finance” category on the App Store & we’re already pushing toward 50,000 MAU just 2.5 weeks after launch. Running a business at scale is like eating sand mixed with glass while enjoying the most juicy sip of 1947 Cheval Blanc. 1) We built our first mobile app prototype in ~4 days (including all the infra, UX/UI, multi-agent system, etc.). 2) Then we launched it & polished it to “somewhat good” in about a week. 3) 6-7 days after that, it became a “wow, quite nice - I like it!” kind of app. But the most hardcore part begins now: when you suddenly hit some *magic* pain point where people start using your app day & night. This is a completely different game - where there are no tutorials, no playbooks, and (unfortunately) no single LLM can tell you the right direction - because the pain is in a thousand edge cases across a thousand devices (you can’t believe what we’re dealing with daily) Work is 14-15 hours a day. Every day. Learning here? Well... I guess the best I can say is to indirectly quote some post I read online a while ago. It was something like: "Great founders don’t win by having fewer problems. They win by reducing the time it takes to react, learn, and iterate - until the distance between feedback & release feels almost unfair" Proud of the entire @axioma_ai & @0xNeurobro teams for winning hard! Abso-freaking-lutely stunning performance!