Techificial.ai

Most AI content on X/Twitter shows demos without explaining how it actually helps you. We build AI systems that run real businesses that work smoothly in production. Are you tired of watching generic AI content with 0 impact on your work? Want to use AI to get real results in your work? If the answer is yes, then this post is for you. Hi, we’re techificial.ai. We help enterprises and startups create AI systems that deliver better results. 🧵Here’s what you should know about us (read more): techificial.ai

This explains something most people experience but do not notice. When you are stuck, pushing yourself to think more rarely works, as your brain just keeps going over the same thoughts. This is where walking helps break that pattern, with no pressure, no screen, just movement and space. That is when new ideas start coming to you. Sometimes the best way to solve a problem is to stop trying too much and just take a walk, @mariotomich.

Just 8 minutes of walking increased creative output by 60%. Stuck on a problem? Go for a walk.

This is more about changing how value is delivered than replacing consultants. Initially, consultants were people, slides, and recommendations. But now, it is becoming AI, engineers, and actual implementation. I have personally seen that companies do not want advice anymore. Instead, they want results. If Anthropic can directly work inside companies and make things happen instead of just suggesting them, then it will be a major change. In my view, consulting is not going anywhere right now, but it is being rebuilt from scratch, @cryptopunk7213.

anthropic is going after the $300B consulting sector with a new $1.5B consulting arm that seeks to put claude into every mid-size company this is exactly what deloitte, mckinsey, accenture do... but anthropic is cutting them out. ruthless but imo the economics make sense: > anthropic will send applied AI engineers to private equity portfolio companies to create custom-claude solutions... > its a genius model: blackstone alone owns 250+ companies generating $300B in rev, imagine if claude doubles that and takes a fee why? anthropic's biggest revenue earner is enterprise, their CFO: "Enterprise demand for Claude is significantly outpacing any single delivery model." > anthropic teamed up with blackstone, goldman sachs and hellman & friedman, each putting up $300M (ZERO consulting firms in the cap table lol) > private equity become anthropic's distribution model for enterprise. sound familiar...? > thats because openai announced a similar venture 5 months ago but the explicit difference is anthropic is a major stakeholder in this new venture brutal for consultants tbh

This is one of those things people will not think about until it is too late. I have seen that everyone is excited about what AI can do, but very few are thinking about what will happen when it gets something wrong. Unlike normal software, AI mistakes are not always predictable. For me, this looks like the next layer of the AI stack, not just building with AI, but managing the risk that comes with it. If agents make decisions, this kind of coverage will not be optional for long, @nico_laqua.

Today, we're excited to launch AI Coverage (insurance for when your AI messes up). Insurance was built for risks that have existed for decades. AI is creating a new category very quickly.

This says a lot about where we really are. It is not that companies do not want AI, but they do not know how to actually use it in their business. And here, the gap is in execution, not technology. I believe that whoever makes AI simple and usable for regular teams will succeed in a big way. I can see that you are focusing on that, @realpaulsmith.

We just launched a new company with Blackstone, H&F, Goldman, others. $1.5B to bring Claude to mid-size businesses at scale. The demand I see across the economy is outrunning everyone's ability to deploy — ours, our partners', all of it. This is one piece of a very big puzzle.

This looks like the first real look at what an AI company building can be. I have seen most people use AI for small tasks, but this turns it into a full system. Instead of doing everything yourself, you are managing a team that never sleeps. If this works properly, then the main limit moves from doing the work to knowing what to build and how to guide it, and that is a big change, @ndrewpignanelli.

Announcing Cofounder 2: Run an entire company with agents. It's the infrastructure for the one person billion dollar company - orchestrating agents across engineering, sales, marketing, ops, and design. (and yes that's my real grandma in the video)

This is exactly the kind of gap most people don’t notice until they really look at it. AI looks smart until you ask it something on the spot and it gets it wrong. You’re not just building another tool, you’re also fixing the missing piece, which is reliable live data. That’s the difference between interesting AI and useful AI. If this works properly, it will save traders hours every single day and help avoid expensive mistakes, @ManaswaS.

If you ask ChatGPT or Claude about an Indian stock right now, the data is usually wrong or outdated. That was one of the problems I wanted to solve. I've been trading for close to a decade. Every day I'd have screens open in one tab, chatbots in another, NSE site for filings, Telegram groups for alerts, some dashboard for FII/DII data, and a spreadsheet tying it all together. At some point I just started building the thing I actually wanted. aftermarkets.in - stock research platform for Indian markets. Filing alerts hit your WhatsApp or Telegram the moment they're published, with AI summaries so you don't need to read through 40-page PDFs. Live screener with 50+ filters, earnings calendar, FII/DII flows, bulk/block deals, insider trading, & much more. It also plugs into Claude, ChatGPT, and Gemini using MCP - so your AI actually has access to real Indian market data. Everything is free right now. Have a look and let me know what you think. -> aftermarkets.in <-

This is one of those moments where the trend matters more than the exact prediction. Forget about the 60% number for a second, @jackclarkSF. What truly stood out is that AI is getting better at answering questions and doing the work behind the scenes. Also, it can conduct research by running experiments to improve other models at a different level. Right now, we are moving away from AI helping humans build things to AI helping build the next version of AI.

I've spent the past few weeks reading 100s of public data sources about AI development. I now believe that recursive self-improvement has a 60% chance of happening by the end of 2028. In other words, AI systems might soon be capable of building themselves.

@xai This is superb 🤯 Can't wait for the next update 💪 @xai

Voice Cloning is now live via the xAI API! Create a custom voice in less than 2 minutes or select from our library of 80+ voices across 28 languages to personalize your voice agents, audiobooks, video game characters, and more. x.ai/news/grok-cust…

This might seem amazing to many people, but there is something most people aren’t aware of. If everything becomes easier to afford, then the question changes from “can we produce more?” to “who owns the production?” There is no doubt that AI and robots can increase output, but that doesn’t mean access will automatically become equal. Not everyone realizes that we already produce enough in many areas. So, the real futuristic question is about distribution than abundance, and that is where things will be decided, @unusual_whales.

Elon Musk has said: "AI/Robotics will mean everyone can have a penthouse if they want. The output of goods & services will be several orders of magnitude higher than today’s economy."

This is one of those things that seems clear once someone points it out. A lot of people think building a product means creating everything from scratch. In reality, most smart builders connect what already exists. The difference is awareness. One person pays $99 a month for a tool, while another uses the same base API for free and builds on top of it. The internet is the same, but the outcomes are very different. If you are starting, this repo alone can save you months and a lot of money, @heygurisingh.

There's a GitHub repo with 323K stars that lists every free API on the internet. The same repo every senior developer has bookmarked but never tells juniors about. It's called Public APIs. 1,500+ free endpoints, organized by category, with auth type and HTTPS status documented for every single one. Here's what's actually in there: → Finance, crypto, stocks, and banking data → Weather, geocoding, maps, and transportation → Books, music, movies, anime, and games → Machine learning, OCR, and sentiment analysis → Government data, open datasets, news, and health → Calendars, currency, security, sports, and dev tools Every entry tells you exactly what you need before you click. Auth type. CORS status. HTTPS support. Direct link to the docs. The dirty secret of the API economy is sitting in plain sight here. Half the $99/month tools on the market are wrapping free endpoints from this repo and selling them back to you with a dashboard. Solo devs are shipping entire products on the free tier of these APIs. Senior engineers know. Juniors are out there paying RapidAPI subscriptions for endpoints that have been free this whole time. 323K stars. MIT License. 100% Opensource.

@BusDownBonnor Looks like Claude made a strong point, @BusDownBonnor.

Claude literally just ended the conversation on me???? This might be AGI

This is the important change most people are missing. I can see everyone arguing over which model is the best. But in reality, the real winners are building how the model is used without announcing it to others. It is like giving two people the same car, where one has maps, rules, and a system, and the other just drives randomly. So who gets there faster? The setup is a system that turns raw intelligence into something reliable, repeatable, and actually useful. Models will keep changing, but the teams that build strong systems around them will keep winning, @code_kartik.

You get one AI agent to automate your business. What do you build first?

"AI will mess up your code and you’ll lose control." That’s the real fear behind all the AI hype. It changes things you didn’t ask for, breaks stuff without any noise, and leaves you figuring out what just happened. Anthropic is trying to fix that with the new Claude Code Desktop. Instead of one long messy chat, you get a workspace where multiple AI agents run in parallel and everything stays visible. So you’re not guessing anymore. You’re seeing. You can: • run separate tasks without mixing context • track every change with clear differences • test, edit, and preview without leaving the app -- Here’s what actually makes it useful: 1. Multi-session workflows - Break complex work into smaller tasks and run them side by side without losing track. 2. Built-in dev environment - Terminal, editor, previews, diffs. Everything in one place, synced with what the AI is doing. 3. Clean context control - Use side chat for quick questions. Keep your main work focused and uncluttered. 4. Workspace you control - Arrange panels your way. Add shortcuts. Connect through SSH when needed. -- End result: 1. You spend less time fixing AI outputs 2. You see exactly what changed 3. You move faster without breaking things That’s the difference between using AI and actually trusting it. -- 👉 Get better control over your code to see exactly what changes, and move faster without switching between tools. Reach out to us at contact@techificial.ai to set up a quick demo this week. Follow us on X/Twitter at @techificial for more updates, insights, and real-world applications. #ClaudeCode #AgenticCoding #ClaudeDesktop #AnthropicAI #BuildingAIthatWorks #TechificialAI

I have seen many people look at benchmarks and assume they are all the same. But in reality, they are very different. Open models might look stronger on paper, but they often need more effort and tokens, and still break when the task changes a bit. In contrast, closed models tend to handle messy situations better. That is the gap most people don’t see. Benchmarks show performance, but real-world use shows reliability. And reliability is what actually matters when you are using these tools every day, @emollick.

This is a good explanation of why the gap between open and closed models is larger than it appears in benchmarks. I would add in that current open models are also more fragile than closed: they handle out-of-distribution problems far less well & have lower emergent capabilities.

This might look exciting for many, but it is far beyond reality. AI is moving faster, no doubt about that. But humans don’t upgrade like software, and gene editing and brain implants all take years of testing, ethics approvals, and real-world proof. What is more likely in the near future is that people who use AI better will move ahead of those who don’t. It is going to be skilled humans versus unskilled ones, and we can already see that happening now, @aravind.

As robots with AI brains outcompete mankind, humans will start using gene editing and neural augmentation to keep pace and better themselves. The real singularity won't be just technological, it will also be biological. Superhumans are coming.

This is the kind of innovation people will only notice when it saves lives. Imagine disaster teams knowing exactly where to go during a cyclone at night without making rough calls. That itself changes outcomes. I have huge respect for the whole team. This is what real progress looks like, @Malay4Product.

Let me explain what just happened today because it deserves so much recognition. GalaxEye is a Bengaluru startup founded in 2021 by IIT Madras engineers. Today they launched Mission Drishti on a SpaceX Falcon 9. It is India's largest privately built satellite at 190 kg. And it carries a technology that no commercial satellite has ever carried before. Normal satellites take photos of the Earth using optical cameras. Like your phone camera, but from 500 km up. The problem is obvious. Clouds. Night. Fog. Smoke. If any of these are in the way, the photo is useless. India has monsoon cover for 4 months a year. That is 4 months where optical satellites are partially or fully blind over large parts of the country. The alternative is SAR. Synthetic Aperture Radar. Instead of taking photos with light, it sends radar waves down and reads what bounces back. Radar goes through clouds, through darkness, through smoke. A SAR satellite can image a flooded village at 2 AM during a cyclone when no optical satellite can see anything. The problem with SAR is that the images look nothing like photos. They look like grainy black-and-white radar maps. A military analyst or a trained geospatial engineer can read them. A farmer, a disaster response team, or a city planner cannot. Until today, if you wanted both optical and SAR data for the same location, you needed two different satellites, passing over at different times, at different angles. Then someone had to manually align and fuse the two datasets. Expensive, slow, and the data never perfectly matched because the satellites saw the same spot minutes or hours apart. GalaxEye put both sensors on one satellite. Optical and SAR, fused into what they call OptoSAR. Three times more information than a single sensor. Processed onboard by an NVIDIA AI chip at 1.8 metre resolution. Now in practice, during the next cyclone hitting Odisha, one satellite pass gives you a clear image of which villages are flooded, which roads are cut, and which buildings are standing. Day or night. Cloud or clear. In near real-time. For defence, it means you can monitor a border area 24/7 regardless of weather. For agriculture, it means tracking crop health across an entire monsoon season without a single cloud gap. For infrastructure, it means monitoring construction progress on highways and bridges without waiting for a clear day. GalaxEye tested their SAR tech on ISRO's POEM orbital platform. The satellite was tested at ISRO facilities. IN-SPACe provided regulatory clearance. NSIL, ISRO's commercial arm, will distribute the imagery globally. And it launched on SpaceX because ISRO's PSLV doesn't have the right orbit slot for this mission. Yes, four IIT Madras graduates built a world-first satellite in 4 years in Bengaluru. Take a bow!

Switching tools usually kills your flow. You end up setting things up again, fixing configs, and sometimes even losing work. I’ve seen many people quit halfway because of this. If I can move my setup in a few clicks and keep going without the hassle, I’ll definitely give it a try, @OpenAI.

Bring your workflow to Codex in just a few clicks. Import settings, plugins, agents, project configuration, and more so you can keep working with fewer interruptions. Your move.

This is the kind of story most people miss when they think AI is just chatbots. She is working on problems like weather, disasters, and climate that affect real life. Faster predictions can save time, money, and even lives. The combination of classical dance and hardcore math says a lot about how talent actually develops. I really want more people to see this side of AI, @shekhu04.

Meet Anima Anandkumar (Every time AI predicts the weather faster than supercomputers, she made that possible) > Born in Mysore, Karnataka > Her grandfather was a mathematician. Science ran in the family. > Studied Bharatanatyam for years alongside engineering > B.Tech from IIT Madras, 2004 > PhD from Cornell University, 2009. > Postdoc at MIT. > Principal Scientist at Amazon AWS. > Helped launch Amazon SageMaker. > In 2017 became Bren Professor at Caltech > The youngest named professor in Caltech's history > Joined NVIDIA as Senior Director of AI Research in 2018 > Invented Neural Operators, AI models that solve complex physics 1000x faster than traditional methods > Built the first AI based high resolution weather model > Now running at premier weather agencies around the world > IEEE Fellow, Alfred P. Sloan Fellowship, NSF Career Award Faculty fellowships from Microsoft, Google, Facebook and Adobe. All four. > Cited over 69,000 times in research papers worldwide A girl from Mysore who danced Bharatanatyam is now teaching machines to understand the laws of nature. "AI is not just about language. It is about understanding the physical world." She built that belief into reality. Equation by equation.