ANKIT NARAYAN SINGH ⚡️

For Data Science and Machine Learning enthusiasts out there !! fb.me/1Xrp24bI5

Three weeks ago I shared that Claude had shocked Prof. Donald Knuth by finding an odd-m construction for his open Hamiltonian decomposition problem in about an hour of guided exploration. Prof. Knuth titled the paper Claude’s Cycles. The story didn't end there. The updated paper shows the story got much bigger. For the base case m=3, there are exactly 11,502 Hamiltonian cycles. Of those, 996 generalize to all odd-m, and Prof. Knuth shows there are exactly 760 valid “Claude-like” decompositions in that family. The even case, which Claude couldn’t finish, was then cracked by Dr. Ho Boon Suan using GPT-5.4 Pro to produce a 14-page proof for all even m≥8, with computational checks up to m=2000. Soon after, Dr. Keston Aquino-Michaels used GPT + Claude together to find simpler constructions for both odd and even m, by using the multi-agent workflow. Dr. Kim Morrison also formalized Knuth’s proof of Claude’s odd-case construction in Lean. So yes: the problem now appears fully resolved in the updated paper’s ecosystem of human + AI + proof assistant work! We went from one AI solving one problem to a full mathematical ecosystem (multiple AI systems, multiple humans, formal verification) running in parallel on a problem that stumped experts for weeks. We are living in very interesting times indeed. Paper (updated): www-cs-faculty.stanford.edu/~knuth/papers/…

This is huge if it works as promised!!

The human traits AI can’t replicate and why they are worth developing… By @ForbesTechCncl expert panel from @alloyautomation @ankitnarayan1 @createdbyjannn and more… forbes.com/councils/forbe… @BetaMoroney @Nicochan33 @enilev @mvollmer1 @mikeflache @antgrasso @FrRonconi @ramonvidall @baski_LA @AkwyZ @Khulood_Almani @sijlalhussain @PawlowskiMario @pierrepinna @sonu_monika @mvollmer1 @sallyeaves @NevilleGaunt @Corix_JC @enricomolinari @Shi4Tech @wcrpaul @RagusoSergio @RLDI_Lamy @NigelTozer @EstelaMandela @JagersbergKnut @DrFerdowsi @PerBBerggreen @sir4K_zen @AmitChampaneri1 @FmFrancoise @HLStockenstrom @ILoveBooks786 @Hana_ElSayyed @CurieuxExplorer @HaroldSinnott @SegundoConnect @pchamard @trudydarwin

The Human Traits Ai Cant Replicate And Why Theyre Worth Developing hubs.li/Q046_npc0 from @alloyautomation @ankitnarayan1 @createdbyjannn and more

Five Valuable Engineering Skills For The AI-First World (Before Research Catches Up) hubs.li/Q046DRXc0 Written by @ankitnarayan1 of @paralleldots

Just got featured in the Most Promising Leaders To Watch in 2026: Redefining Leadership for a Changing World bombaytimes.com/lifestyle/vira… @bombaytimes

Dhruv Bajpai, @Accenture ; Ankit Narayan Singh, @ParallelDots ; Sandeep Jabbal, @shoppersstop ; Praveen Govindu, Delloite & Vikraman Sridharan, @Lenskart_com , offered insights into "The Phygital Renaissance: Architecting India's "Intelligence-First" Retail Ecosystem"

This is an excellent piece on how to think about Forward Deployed Engineers (FDEs) for enterprise AI startups. My favorite part is right at the end - “Linear services scale by adding bodies. Exponential services scale by adding capability. Both have FDEs. Only one is building something that compounds. If the product isn’t improving, you don’t have forward deployed engineers.”

Introducing Adam, the first AI mechanical engineer

@gokulr Companies rarely go under because of hiring delays, but they frequently collapse when they run out of money to meet payroll

Exactly. Another way to put it, as Gaurav Vohra does in his great article, is: “Stop Hiring People”.

@gokulr That benchmarking is how you find where you’re still meaningfully better and should double down. In our case, we learned general VLMs lack the fine-grained object recognition our customers need but excel at context, so we combined both into a unique stack.

VERTICAL AI CHALLENGE Vertical AI Founders: You've spent 2+ years building your agents, training your model on your customers' data, embedding into workflows, creating a powerful GTM motion, all the best practices. You've beaten back challengers and are the #1 or #2 player in your vertical. I'm sorry, you cannot relax. In fact, you need to massively up your game. Turns out you are facing an existential challenge: long-horizon agents (eg: Claude Code). Agents that are not trained on a specific domain, but can reliably work for hours or days on end in pursuit of a goal, self-correct, and actually do stuff. I'm sure many Vertical AI founders will say: "Oh, we are not worried. We are the system of record for decision traces. We train on enterprise-specific context. That's why these horizontal agents can never catch up with this." You might well be right. But, but, but ... you cannot afford to bury your head in the sand. These long-horizon agents will get better very, very quickly. You need to understand precisely how good they are at the exact jobs you've built your agents on. You cannot wait for someone else to do this. For example, if you're a legal AI company with an agent that automates contract review, you must compare how good your specialized agent is versus a general-purpose long-horizon agent that's simply given the contract and asked to perform the same review. My challenge to you: Assign a strong engineer on your team to focus 100% on using long-horizon agents (with minimal context, other than just the contract in the example above) to compete with your custom-trained agents. Benchmark how the long-horizon agents perform vs your agent. Rinse and repeat it every few months. Like with most other things worth measuring, what matters is the rate of improvement (the "slope" vs the Y-intercept). If the long-horizon agent is 30% as good as your vertical agent on Day 1, but 50% as good on Day 60, and 70% as good on Day 120, you need to reassess your product strategy. AGI is coming for everyone. Long-horizon agents are the closest we have to AGI, and as a Vertical AI company, you need to figure out how you compete and survive. Game on.

@mattturck Hallucinations in vibe coding tools is still a big issue, for sure no software developer is getting replaced unless something improves dramatically there!!

It’s remarkable that AI hallucinations went from the biggest topic just a year or so ago to a largely fixed problem people barely talk about anymore today.

It's not enough to build software with AI, you need to be a 10x AI software engineer. All the best teams are parallelizing their AI software use and churning out 25-50 meaningful commits / engineer / day. The Claude Code best practices guide is an absolute goldmine of tips

@hamzaxwz Tried @magicpatterns and liked what they could create for my use-case!

There's not a single AI right now that can generate a decent looking UI design Prove me wrong.

@GooglePlay our app has been delisted from on Play Store and we have been having a hard time to relist, can you please help? We have submitted the identity documents

@ankurnagpal Would love to have it, thanks!

I wrote a detailed 20,000 word guide on Personal Finance for Startup Founders Covers how much to pay yourself, hiring a team, raising venture capital, running finance, QSBS, selling secondaries and exiting your business Leave a comment and I'll DM you a copy

Meet Ankit Narayan Singh, co-founder and CTO of ParallelDots, sharing his vision for the future of image recognition solutions and insights into ParallelDots’ innovative journey, reshaping the FMCG and retail landscape with cutting-edge technology.   #JioGenNext

While there are 1000s of angels and endless startups, there hasn’t been an easy way for either to discover each other, we at @Dzerovc thought of taking a jab at it. We created angelinvestorsinIndia.com a repository of angels in India, so founders could reach out to them

@jfbrly @jasonlk This is possible today even with open-source LLMs but companies relying solely on a wrapper over chat GPT won’t succeed. Enterprises require bespoke solutions. SIs like Accenture stand to profit more than SaaS solutions with flashy websites and slick demos.

There’s an AI Backlash happening now in the enterprise in particular Many vendors have promised big results and not delivering, especially in automation Promising to automate 50% of your contact center with AI, of your interactions with AI, etc. No one is hitting it The genie isn’t going back in the bottle, customers are committed to the goal. But vendors have way over promised across the board.

@jasonlk In my industry, since 2014, vendors have touted 98% accuracy in retail shelf SKU detection using computer vision. These claims turn clients into skeptics, making it tough for genuine companies to showcase their superiority amidst empty marketing promises being made daily!