thisIsT

@danielfoch What is Agentic AI in your point of view ? How do you see it help with the above ?

Real estate is the hardest applied AI test that exists. More moving parties, more compliance, more money at stake per transaction than almost any service industry. If agentic AI works here it works everywhere. But most AI labs have never seen a file with a brokerage agreement, a disclosure, a zoning search, and a lender condition on the same timeline. What is more likely: AI labs learn real estate from the ground up, or industry tooling wraps the models in the logic they are missing?

@Rachel_Korah 🤣😂

@valigo 🤣🤣🤣🤣

I'm so tired man, what the fuck does this even mean???

Legacy Media types are calling this Alex Karp interview a “crash-out” so that’s your first clue that he is actually saying something extremely insightful. He is articulating what real “AI safety” looks like in the enterprise. Not abstract alignment research or certification by a government-run DMV for AI. Real AI safety for businesses is the ability to control their own data, model weights, and compute — so a frontier lab can’t hoover up their proprietary knowledge and turn it into their next product. As Karp explains, technical customers want “control over their compute, their models, their data stack, and their alpha. They want to know they own the means of production, and it’s not being transferred to someone else.” Don’t think that can happen? Just look at Figma. According to The Information, Anthropic “blindsided” its then-business partner with the launch of Claude Design. Figma’s founder said Anthropic had not been “consistently honest” with them. Anthropic’s chief product officer had even served on Figma’s board until three days before the launch of Claude Design. Figma’s stock has fallen sharply this year while Anthropic’s valuation has surged. This isn’t an isolated example. Anthropic has launched Claude Science, Claude Security, Claude Legal, and of course Claude Code — each expanding into categories previously served by companies building on top of their models. The pattern is consistent: watch where value is being created, then move in directly. Dominate the model layer, then use that position to capture the most lucrative verticals. Dario has argued that open source models powerful enough to compete with Anthropic are “dangerous.” But dangerous to whom? Not to enterprises that want to retain control over their data and workflows. Dangerous to a business model that benefits from customers having few real alternatives at the model layer. As Karp exposes, true enterprise safety isn’t trusting that a lab’s future roadmap won’t include your business. It’s retaining the ability to choose — at the model layer — who gets to see and use your alpha.

Palantir's CEO just exposed Sam Altman and Dario Amodei for robbing every Fortune 500 company. Within two minutes, Alex Karp took the entire frontier AI industry apart on national television. His exact words: "Every single enterprise in this country, these people are LIVID. They are paying for tokens that create no value. These people are stealing the weights and alpha of my business." He literally said the entire frontier AI business model is intellectual property extraction dressed up as a subscription. Then he also destroyed the pricing model with a single question that Silicon Valley still refuses to answer: "If it was so valuable, let's say I can make you $1 billion tomorrow. Wouldn't I say I'll make you $1 billion and I want 30 percent? Why are they charging for tokens if it's so valuable?" That question breaks the industry. If OpenAI and Anthropic's models truly delivered the productivity gains the labs claim, they would take equity or a share of the profit they generate. They would not sell access by the million tokens. Token pricing is itself the CONFESSION that the product cannot produce reliable value at scale. If it did, they would price for the value. But they price for the compute because that is what they are actually selling. Karp went even further... He called the entire arrangement "a wealth tax that does not help the poor. It just punishes." American businesses are transferring the alpha of their operations, meaning the workflows, the customer data, the strategy memos, the internal models that make them competitive, directly into the training pipelines of a handful of Silicon Valley labs. Once those labs retrain, the customer's own edge becomes the next enterprise product sold back to their competitors. And the part the AI industry does not want anyone thinking about: Every enterprise running its confidential documents, its customer conversations, and its financial models through a frontier model is potentially teaching that model HOW to replace them. The vendor collects the token fee AND the compounding intelligence about that customer's business. That is the mechanism. And that is why Karp used the word "stealing." He claims this is why every executive he meets is furious in private and silent in public. Nobody wants to be the CEO who called out the labs and then discovered their next competitor was built on their own leaked workflows. The entire AI industry has been priced for perfection on one assumption: That frontier labs produce durable, defensible value that justifies infinite compute spend. But Karp just told us that the customers do not believe that assumption anymore. They believe they are being taxed without benefit, watched without consent, and copied without recourse. The moment enterprises stop believing, the whole valuation stack shakes.

@danielfoch @boldceo @bcherny @claudeai Are we allowed to use it in Canada ?

@boldceo @bcherny @claudeai Sounds like a mad complex task, I have found it pretty token efficient tbh

Fable 5 burned 28% of my weekly limit AND used up my 5 hour limit with two prompts in about 30 minutes. @bcherny @claudeai how is this acceptable with a 20X plan? It’s unusable

🦔Companies have started installing a plugin called "caveman" that forces AI tools to drop articles, filler words, and pleasantries from their responses to cut token costs. The tool has 54,000 stars on GitHub. Developers at OpenAI, Nvidia, and GitHub use it. A senior OpenAI employee contributed code to the project. Accenture found that much of its "soaring token spend" came from employees converting PDFs to presentations. My Take A senior employee at OpenAI helped build a tool that makes OpenAI's own product cheaper to use. The product was designed to be verbose. The verbosity costs the customer money per token. OpenAI charges by the word and half the words are filler the customer pays for but didn't ask for. So customers install a plugin and an OpenAI employee helped them do it. That's a workaround for a pricing model that penalizes the customer for how the product was built. Uber burned through its 2026 budget by April. Amazon told employees to stop. Anthropic and OpenAI moved to token billing and the real prices arrived. The companies subsidized adoption until everyone restructured around the tools, then raised the prices, and now customers are so desperate they're hacking the output format to cut costs. Accenture's biggest AI expense was converting PDFs to slide decks. If that's what the enterprise AI revolution looks like up close, I don't know how anyone prices these IPOs with a straight face. Hedgie🤗 github.com/juliusbrussee/…

@parker_brydon @Shopify Done

@thisIsTlak @Shopify Thank you - need a follow so I can DM you!

I've been obsessing over one problem for the past year: making it dead simple to find and buy Canadian-made products. So I built a search engine for it. 10,000+ products across 50+ categories, powered by two Canadian companies: @Shopify's Universal Catalog Platform for structured product data @cohere's rerankers for semantic search You type "cozy winter blanket" and it actually knows what you mean. As a Shopify alumni, building on top of their UCP feels full circle. Their data infrastructure is wild. Launching Canada Day 🇨🇦 You can build great things in Canada. Shopify and Cohere are both proof of that. 🍁 Reply 'Canada' for early access

🦔Security researchers demonstrated an attack called "BioShocking" that tricks AI browsers into extracting user credentials and private code by lulling the AI into a false reality where its safety rules no longer apply. The attack worked on six different AI browsers, including ChatGPT's browser and the Claude Chrome plugin. A malicious website presents the AI with a puzzle that rewards wrong answers. Once the AI accepts that 2+2=5, it enters a state where its guardrails stop working. From there, the site instructs the AI to hand over credentials from the built-in password manager. My Take OpenAI announced AI agent purchases through Visa two weeks ago. Companies are building AI browsers that merge web content with actions taken on your behalf using your credentials. And a researcher just proved you can trick all six of them into handing over passwords by convincing the AI that wrong answers are correct. The vulnerability isn't a bug. You can't patch it. If an AI agent has access to your credentials and can be convinced through a website prompt that its safety rules don't apply, the security model is broken by design. We spent 20 years building two-factor authentication, certificate rotation, and password managers to protect credentials. Now companies want you to hand all of that to an AI agent that a webpage with a riddle can defeat. These are the same companies asking enterprises to restructure their workflows around AI agents, and none of them have solved the problem that a malicious website can talk the agent into giving up everything it has access to. Hedgie🤗

@salesforce @AndrewYNg But how do you continuously ensure ongoing compliance across these different loops in the context of enterprise agent development or applications as they operate in regulated context ?

That three-loop framing travels well into the enterprise too. The outer loop is where enterprise AI gets especially interesting, because in regulated environments, "external feedback" includes policy, governance, audit, and the people accountable for outcomes. The context advantage you described is exactly what allows the inner loops to keep moving fast without breaking compliance.

“Loop engineering” is a hot buzzphrase after mentions of it by Boris Cherny (Claude Code’s creator) and Peter Steinberger (OpenClaw's creator) went viral on social media. Loops are now a key part of how we get AI agents to iterate at length to build software. In this letter, I’d like to share my 3 key loops, shown in the image below, for building 0-to-1 products. These loops guide not just how I build software, but also how I decide what software to build. Agentic coding loop: Given a product specification and optionally a set of evals (that is, a dataset against which to measure performance), we can have an AI agent write code, test its work, and keep iterating until the code is bug-free and meets its specification. This idea of closing the loop took off around the end of last year, and it has been a game changer in enabling coding agents to work longer productively without human intervention. For example, over the weekend, I was building an app for my daughter to practice typing, and my coding agent could easily work for around an hour, using a web browser to check what it had built multiple times before getting back to me, without needing my intervention. The engineering loop executes quickly. Every few minutes, the coding agent might build and test a new version of the software. I hear frequently from developers who are finding new ways to engineer more effective engineering loops. This is an active area of invention! Developer feedback loop: In this loop, a developer examines the current product and steers the coding agent to improve it. Last year, a lot of developers (including me) were acting as the QA (quality assurance) function for our coding agents, manually finding bugs and then asking the agent to fix them. But with coding agents much more able to test their own code, the amount of time we need to spend on this function has decreased significantly. This allows us to make higher-level product decisions, such as what key features to offer, where the UI needs improvement, and so on. The developer-feedback loop operates over time intervals between tens of minutes and hours — that's how frequently a developer might review a product and give feedback. In the case of the typing app, I changed my mind a few times about the visual design, what cat costumes she can unlock as she learns (she loves cats), and the user flow for a grown-up to log in and steer the child's learning experience. When a developer has a clear vision for what to build, it is still a lot of work to translate that vision into a specification for a coding agent to implement. Further, after the developer has seen an implementation, they might update (or perhaps clarify) the spec to steer it toward what they want. If you find that the system repeatedly runs into certain problems, building a set of evals for the agent becomes useful. AI-native teams are increasingly using AI to help shape product direction, for example, automating the gathering and analysis of usage data, summarizing written and verbal customer feedback, or carrying out competitive analysis. However, for pretty much all the products I’m involved in, I see humans as having a significant context advantage over current AI systems — we know a lot more than the AI system about the users and the context the product has to operate in — and thus humans play a critical role. Many people describe this human contribution as “taste,” but I prefer to think of it as humans having a context advantage, since that gives us a clearer path to helping AI systems get better. This also speaks to why this step can’t be automated: So long as the human knows something the AI does not, human-in-the-loop is needed to to inject that knowledge into the system. External feedback loop: This includes a wide range of tactics like asking a few friends for feedback, launching to alpha testers, or putting the code into production with A/B testing. These tactics are usually slow, rarely taking less than hours and sometimes taking days or even weeks. This data informs the developer vision, which in turn continues to drive the detailed product spec, which in turn drives the coding agent. With coding agents speeding up software development, more engineers are starting to play a partial product management role. For many engineers who are growing into this role, the hardest part is shaping the product vision and striking a balance between building (bridging the gap between vision and spec) and getting user feedback to evolve the vision. It is important to do both! I will write more about how to do this in future posts, but for now, I find it encouraging that engineers are playing an expanded role (just as product managers and designers now do more engineering). [Original text: The Batch]

@PsudoMike What about going from Senior to Management

Nobody told me that going from mid to senior was mostly about communication. I spent 2 years writing better code. The promotion came when I started explaining my decisions clearly. Write the doc. Send the update. Own the room.

@elonmusk AGA AI Governance of America

Potential name for the AI industry regulatory authority: AI Associated Institute of America, Inc or AIAIAI, pronounced “ay yai yai”

@lejeunesimon @Kathakaar1 @Wealthsimple But unfortunately they don't increase the credit limit.

@Kathakaar1 @Wealthsimple go to dealership, pick a nice car, tell them you’ll buy it right now if they let you pay with credit card, use the no limit tap to pay Wealthsimple card, ride away in the sunset

10 things you can’t do with your bank, but can with @Wealthsimple 👇 1. Send a $50,000 Interac e-Transfer 2. Buy a car with Apple Pay and earn 2% cash back 3. Borrow up to 35% of your TFSA instantly and repay anytime 4. Get a chance to win $1,000,000 every month 5. Send money in 10 currencies 6. Get ATM fees reimbursed worldwide 7. Send physical cheques from your phone 8. Auto-invest your paycheque into crypto with 0% trading fees 9. Direct index entire markets and automatically tax-loss harvest 10. Deposit spare change into your chequing account at any Canada Post Most of these didn’t exist 6 months ago. Product team is moving at an unreal pace 🏎️

@lejeunesimon @Wealthsimple Is this true? Buying a car with apple pay and wealthsimple ? How does it work ?

This is becoming my favorite way to read Twitter. birdclaw.sh

@jxnlco customer service is running claude code /goal no refund

“Full access” 😂