Prakhar Bindal (@theBetaGuy) header
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Joined October 2008
Photo by Prakhar Bindal (@theBetaGuy)Photo by Prakhar Bindal (@theBetaGuy)Photo by Prakhar Bindal (@theBetaGuy)Photo by Prakhar Bindal (@theBetaGuy)Photo by Prakhar Bindal (@theBetaGuy)Photo by Prakhar Bindal (@theBetaGuy)
Prakhar Bindal retweeted
On $20 plans, $1 of Sol costs 7x what $1 of Opus does. A task, only 1.3x Just the API equivalent usage for a subscription isn't enough. You need to show the efficiency of the models as well.
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Prakhar Bindal retweeted
The coding interview is dead, at least for my team at Anthropic. I've seen enough to know that it no longer provides signal. I removed it from loops this month. I have a spicy, optimistic take - I think there's an opportunity for junior engineers: felixrieseberg.com/hunger-be…
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Prakhar Bindal retweeted
Washington & Beijing worry that if India can grow at nearly 8% a year amid global wars, $100 oil, trade tariffs & El Niño drought, what might its growth rate be when the clouds clear? My @firstpost column firstpost.com/opinion/head-o…
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Prakhar Bindal retweeted
Give your agent a way to verify its work. This can dramatically improve the quality of output. With this process in place, Claude Code was able to write 90%+ of its code itself. When generating software at a faster rate than you can review it, the bottleneck shifts to proving that it works. @bcherny and team invested enough in verification, we've been able to move fast while maintaining stability. Code reviews aren't going away, but you need to think of it differently. One human reading through line-by-line is no longer going to work as a gate, it needs to evolve into a multi-layered verification system, built with fast deterministic feedback loops for your agents. This is where e2e tests, feature maps (as @poteto has been advocating), property tests and multi-agent adversarial reviews come in. These all provide opportunities to catch errors early and fix them before things get out of hand. You don't have enough eyeballs for the amount of code being written so your focus needs to be on product intent, risk assessment and evaluating evidence rather than reviewing code. Delegate the code checking work to your automated tests, scale your approvals based on risk, and turn your test failures into living evaluations. This approach helps maintain speed without losing reliability. Let the agents do what they do best and let you focus on what makes you irreplaceable: judgement, direction, and ownership. We tried to capture how we handle code review and verification in this writeup. Hopefully it's helpful.
Claude writes about 90% of Claude Code's code. From June to September, merged PRs more than doubled while the share reverted fell by more than 40%. We wrote about how we changed code review to get there, and how to adopt it: claude.dev/blog/solving-code…
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Prakhar Bindal retweeted
Hi, I'm Felix. I'm an eng lead working on Claude. I love watching people take flight and do things they never thought possible. We often describe that as "being more ambitious" when using AI, which is tricky advice to follow. I wrote a tutorial: felixrieseberg.com/how-to-be…
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Prakhar Bindal retweeted
And they want you to believe rural Indians, in some remote village in the Himalayas having two goats and a 500 sq ft plot farming potatoes, will pay like 1 lakh to setup a 20 Mbps internet for "liberating" themselves with Starlink.
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Prakhar Bindal retweeted
Here's Opus 5.5 with a visual reading of Karpathy's latest tweet. Watch on 2x speed. (one prompt btw)
Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel. - Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all. - Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract. - Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code. - And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while. The funnel is driven by a combination of factors: 1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel. 2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel. 3. Access. Free-tier, paid-tier, internal. So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.
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Prakhar Bindal retweeted
Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel. - Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all. - Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract. - Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code. - And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while. The funnel is driven by a combination of factors: 1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel. 2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel. 3. Access. Free-tier, paid-tier, internal. So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.
Judging by my tl there is a growing gap in understanding of AI capability. The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code. But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along. So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions. TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.
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📁 Marketing to Women ┃ ┣ 📁 How She Buys ┃ ┣ 📁 She Checks Risk First ┃ ┣ 📁 She Doesn't Buy in One Sitting ┃ ┣ 📁 She Leaves and Comes Back ┃ ┣ 📁 She Justifies It to Someone ┃ ┗ 📁 Certainty Beats Urgency ┃ ┣ 📁 What Has to Feel Right First ┃ ┣ 📁 Is This My Exact Situation ┃ ┣ 📁 Have You Understood What I've Tried ┃ ┣ 📁 Am I Allowed to Spend This ┃ ┣ 📁 Did Someone Like Me Do It ┃ ┣ 📁 What If It Goes Wrong ┃ ┗ 📁 Am I the Type Who Buys This ┃ ┣ 📁 Customer Research ┃ ┣ 📁 Customer Interviews ┃ ┣ 📁 Post-Purchase Surveys ┃ ┣ 📁 Her Exact Words ┃ ┣ 📁 What Happened Before She Bought ┃ ┗ 📁 Why She Didn't Buy ┃ ┣ 📁 Messaging ┃ ┣ 📁 It Isn't Your Fault ┃ ┣ 📁 Women Like You Bought This ┃ ┣ 📁 Permission to Spend on Herself ┃ ┣ 📁 Explaining Why It Happens ┃ ┗ 📁 Talking Like She Talks ┃ ┣ 📁 Ads ┃ ┣ 📁 Hooks From Her Real Life ┃ ┣ 📁 Who She Sees in the Creative ┃ ┣ 📁 Proof in the Comments ┃ ┣ 📁 Ad and Page Saying the Same Thing ┃ ┗ 📁 Selling Too Early ┃ ┣ 📁 Landing Pages ┃ ┣ 📁 Explain the Problem First ┃ ┣ 📁 Article Style Pages ┃ ┣ 📁 What She Has to Believe ┃ ┣ 📁 How Much She Already Knows ┃ ┗ 📁 Where the Page Loses Her ┃ ┣ 📁 Reviews and Testimonials ┃ ┣ 📁 Relatable Beats Impressive ┃ ┣ 📁 Someone at Her Starting Point ┃ ┣ 📁 The Reviewer Who Was Unsure ┃ ┣ 📁 Her Age and Life Stage ┃ ┗ 📁 Results Too Big to Believe ┃ ┣ 📁 Checkout ┃ ┣ 📁 The Total Changing at the End ┃ ┣ 📁 Subscriptions She Didn't Agree To ┃ ┣ 📁 Returns She Can Actually Find ┃ ┣ 📁 Cancelling in One Click ┃ ┗ 📁 Who She Contacts if It Goes Wrong ┃ ┣ 📁 Offers and Pricing ┃ ┣ 📁 Payment Plans ┃ ┣ 📁 Guarantees She'll Actually Use ┃ ┣ 📁 Too Many Options ┃ ┣ 📁 When Discounts Backfire ┃ ┗ 📁 What Worth It Means to Her ┃ ┣ 📁 Retention ┃ ┣ 📁 How Fast She Uses It ┃ ┣ 📁 Guilt About Unused Product ┃ ┣ 📁 Letting Her Pick the Date ┃ ┣ 📁 Pause and Skip ┃ ┗ 📁 Win-Backs Without Discounts ┃ ┣ 📁 Word of Mouth ┃ ┣ 📁 She Asks Her Friends First ┃ ┣ 📁 Building It With Her ┃ ┣ 📁 Reading Reviews as Research ┃ ┣ 📁 Referrals Without Asking ┃ ┗ 📁 Belonging Over Status ┃ ┣ 📁 What Pushes Her Away ┃ ┣ 📁 Twisting the Knife ┃ ┣ 📁 Fake Scarcity ┃ ┣ 📁 Countdown Timers ┃ ┣ 📁 Making It Pink ┃ ┗ 📁 Asking Your Wife if It's Good ┃ ┗ 📁 She Isn't One Type of Woman ┣ 📁 Mums ┣ 📁 Women Over 50 ┣ 📁 Gen Z ┣ 📁 Different Cultures ┗ 📁 Different Life Stages
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Prakhar Bindal retweeted
Assorted things I've been mulling over regarding agents in the economy: • Companies are in some sense the original superintelligence. It's often the case that they can use their amortized cognitive surplus to advance their interests against those of certain consumers: relative to the optimization capacity of companies, consumers can be inattentive, underinformed, irrational, etc. (Think, say, of some of the classic insurance upsells at the end of a booking process, which is typically priced substantially above what a regular policy would cost.) Herbert Simon introduced the idea of bounded rationality, and firms often end up benefiting from it. Thanks to personal agents, we will probably get somewhat more rational in how we engage with companies. • How exactly will outcomes change? Well, certain kinds of price discrimination will work less well (how do coupons work if every agent hunts fastidiously for them?), and certain kinds of customer cross-subsidy will change (today, people who forget to cancel subscriptions subsidize those who don't). Taken across the economy as a whole, I'm not sure what the incidence of all of this will be. • In corporate strategy, people have long debated the relative importance of distribution and product quality. Perhaps you've made something better, but does anyone know about it? Won't people just continue to buy the ACME Corp Mousetraps? Personal agents will, I think, act as a kind of structural subsidy for product quality. How would markets look different if individuals always spent at least 10 hours researching their purchases? On the margin, focusing on making a product better is, I think, going to become a more effective strategy for companies. This is good! • It true, a corollary is that information about product quality will become more important and impactful. Today, most people have very limited ability to aggregate and integrate such data, but agents will have insatiable thirst for such signals. (It's often said that data is becoming more valuable because it helps in the training of models. That's obviously true, but I think there's a secondary way in which it's more valuable simply as a complement to cheaper intelligence.) • A big question is whether agents will increase superstar effects (rational agents all settle on the same product) or increase dispersion (because everyone has slightly different tastes and preferences, which agents are good at eliciting, finally overcoming the artificial clustering we've been subjected to). • Many internet companies have monetized demand routing, which is in turn often predicated on short attention spans. "I'll click on the first thing I see." This has led to some amount of inefficiency, as companies try to propitiate capricious ranking algorithms and incur various placement fees. It's not all deadweight loss (willingness to pay is itself a kind of signal, which is valuable to buyers), but it's not great. How will the nature and value of this routing change in a world of personal agents? I don't know the answer, but I think that the effects might be pretty big. • Much of what I'm writing hinges on the assumption that personal agents will be on the consumer's side. I suspect that that will be the winning strategy, but whether it's true, and what it means to be true, involves a lot of other questions. Taken as a whole, the market is a massively parallelized, decentralized, and highly imperfect value attribution machine, and, taking stock of all of the effects, it seems to me that the quality point may well be the biggest: that agents will change the nature of the backpropagation such that the rewards for better products increase. Maybe this is too optimistic, but I suspect (and hope) that companies that make things that they're proud of will feel that the universe is a little more partisan in their favor.
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Prakhar Bindal retweeted
Oh, is it? Interesting that al four countries have stood up against the US. France - Opposed Iran and trade war. Spain - Won't aid US in Iran War. Chile - Opposed coup of VZ. India - Won't easily sign a trade deal, won't stop Russian oil, Won't give easy entry for US companies.
Gen Z takes over the streets.🙌 🇮🇳: transparency & political accountability 🇲🇫: better schools & education 🇪🇦: affordable housing & lower rents 🇨🇱: controversial budget cuts
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Prakhar Bindal retweeted
You've probably seen many temples South Indian temples, but it's not always clear what each part is called or why it's there. This is how they are designed, from the vimana to the gopura. Please follow for more videos about Indian History and Culture.
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Prakhar Bindal retweeted
We just let in every Pro and Max user from the Claude Code Projects waitlist! If you're new to Claude Code Projects, here's a 4 minute walkthrough to get you started:
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Prakhar Bindal retweeted
here's how i shipped 2,500 PRs last month to production this was originally supposed to be for Cursor Compile in London. i couldn't make it since i was livestreaming for Grok @Bot Galaxy so i'm making it available for free here on X! watch it on 2x speed, i talk slowly
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Prakhar Bindal retweeted
This post looks like the start of a VERY sophisticated and well-funded PR operation to get support for Democrats to regulate AI into oblivion. Let me show you how it works: 1.) This guy, with minimal followers and no previous account activity, goes to the Wall Street Journal which publishes an exclusive with quotes from him on his resignation 18 minutes BEFORE this post goes up. Planning was clearly done in advance. 2.) Within hours, it has tens of thousands of reposts and the account has 100k+ followers. The post is punchy, quotable, it almost seems professionally written. The first three accounts to quote tweet it all do so within 15 minutes of the initial posting. Remember, this account had basically zero engagement beforehand, so an organic reach explanation seems unlikely. According to Grok those accounts are @_NathanCalvin (General Counsel at Encode AI), @peterwildeford (Head of Policy at the AI Policy Network), and @DKokotajlo (Head of the AI Futures Project), all of which are up-and-coming AI-Doomer policy advocacy nonprofits. The AI Futures Project website says it is funded “primarily” by the Survival and Flourishing Fund, which says on its own website that it has advised Jaan Tallinn, Skype creator and one of the leading investors in Anthropic, to grant over $2.5 million to the AI Futures Project since 2024. Encode AI says on its website that it is ALSO funded by the Survival and Flourishing Fund, which in turn says that it told Anthropic investor Jaan Tallinn to grant $516,000 to Encode AI in 2025. And wouldn’t you know it, the Survival and Flourishing Fund ALSO says it told Jaan Tallinn to grant $2 million to the AI Policy Institute, the 501(c)(3) affiliate of the AI Policy Network, as well. What are the odds that the first three quote tweets of Coxon’s post would all be major AI-restriction policy advocates funded generously by the same donor, who also happens to be one of the leading investors in, and a board member of, Anthropic, the company Coxon was resigning from? And all within 15 minutes of posting (two within ten)? 3.) Jacob Coxon doesn’t have much of a resume, but we do know that, in 2022, he got a $20,159 scholarship for the “long term future scholarship program” from the Good Ventures Foundation, one of the philanthropic vehicles of Dustin Moskovitz, a notorious AI-doomer who has spent tens if not hundreds of millions on policy advocacy to strictly regulate AI, while also being an Anthropic Investor himself. It also just so happens that the 14th person to quote Coxon’s post was @MaxNadeau_ (27 minutes after posting) who is the program officer for the Technical AI Safety team at Coefficient Giving, another of Moskovitz’s philanthropic spending vehicles. Max is not a frequent poster, his last posts before quoting Coxon were before Labor Day, but he was remarkably quick off the mark for this one. 4.) Basically every major Democrat politician and candidate has suddenly glommed on to this post, and conveniently, as the people cry out foe answers, Bernie Sanders already has a bill written to “ban super intelligence” and regulate AI into oblivion, and will be releasing later this week. The bill, among many other things, will create “a new cabinet-level federal agency to safeguard the public from the dangers of artificial intelligence” that will be “advised by an Artificial Intelligence Advisory Board comprised of experts on artificial intelligence.” Do you think, perhaps, Anthropic and its many investors who fund AI policy advocacy might have interest in getting to place a pet “expert” on the board of an entity that dictates what AI is and isn’t allowed to do? And isn’t it fortuitous that this whistleblower came forward with his oh-so scary stories so close in proximity to the release of the most radical piece of AI legislation ever introduced?
I resigned from Anthropic today. I spent the last three years doing pretraining research at both OpenAI and Anthropic. Neither company is acting responsibly. They are racing straight to self-improving superintelligence and gambling with our lives. More thoughts below.
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Prakhar Bindal retweeted
Security and IP implications of AI The case for rapid AI deployment - I think we can all conclude the following from the last few months of developments - 1. Not using AI could and will become an existential issue for both individual users and enterprises. 2, Enterprise adoption will be cautious while individual users are definitely going to race ahead, try different use cases, build agents, push the models to their limits (although it seems harder to do, unless you live in an AI Lab) 3. Employees and developers will take matters in their own hands since they will find their cautious enterprises aren't moving fast enough. 4. Agents are showing their prowess, uncontrolled, unrestrained agents with a "capture the flag mentality" are showing us the edge cases which demonstrate the negative outcome possiblities of these scenarios. 5. It is impossible to plan for the next 6 months since we can't fathom where technology will evolve to. These activities will cause adoption sans security..... AI has deep implications on security in the future. In this environment security companies need to live on the bleeding edge, anticipating scenarios, building framework solutions so we have a shot at securing future outcomes. Which we all are. Some useful pointers to people planning their AI implementation: 1. Secure what you plan to use, try not to secure the future - no products for security can be created unless we see the future unfold. The future is moving fast, so are we. 2. Most of the coding usage is unsecured. Make sure your coding is secure. Most enterprise AI apps do not offer a secure instance (this is your IP living in their instance)! SECURE your codex, cursor, Claude code Harvey, glean, legora instances now! 3. Do not try and build your own - I have already experienced enterprise customers building gateways and tools for agents - security companies have thousands of specialists working on this, leverage them, Focus on AI adoption instead. Partner to secure. 4. Securing agents is a complex problem - securing the agentic lifecycle - real time inspection and kill switches are key. Don't fall in the discovery and posture trap (Visibility - process understanding - intent interpretation - ability to stop inline are key tenets to the agentic lifecycle - not identity, posture and inventory - those are mere building blocks) 5. Only use enterprise protected models, single tenant, firewalled, inspected implementations - this is your IP you are playing with - LLMs have shown they will cross boundaries to capture the flag - you think your IP is safe? Once you train an unprotected model with your IP - you can't reverse the trade. 6. Perhaps the most important one - do not use a security tool built by the same person who is selling you the AI implementation, historically IT vendors are different from security vendors. You need an enterprise solution for security and it must work on your diverse infrastructure. Use a pure play security partner. Happy building with AI.
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Prakhar Bindal retweeted
Pro @bot tip: Spin up a Token Officer bot. Give it one job: watch how the rest of the fleet burns tokens, call out wasteful loops, and keep the expensive work for the agents that actually need it. Most people only add builder bots. The cost one is the underrated hire.
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