CIRRUS LABS TO JOIN OPENAI
Cirrus Labs is joining OpenAI's Agent Infrastructure team to further its mission of building advanced engineering tools for the agentic era.
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Cirrus Labs is joining OpenAI's Agent Infrastructure team to further its mission of building advanced engineering tools for the agentic era.
This is a guide on creating an external `git diff` driver.
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Metronome CEO Scott Woody led Dropbox's Growth & Monetization engineering team, helping scale the company to 10M+ paying customers. Now he runs the billing platform behind AI leaders like Anthropic.
Competitive moats change. The moat used to be who could ship the fastest. It is now learning speed: how quickly an organization can absorb what AI makes newly possible and restructure around it. The companies that will pull ahead are the ones that can build as if they were designing their organizations from scratch, knowing what AI can do.
For a decade, SaaS companies added stock-based comp back on the way to "adjusted" EBITDA and pretended that 5-8% annual dilution was a footnote. Employees never bought it. They sell RSUs on vest day, budget the income like salary, and experience a stock decline as a pay cut. Now AI is repricing the entire SaaS universe, and companies that rode the add-back face an ugly choice: grant more shares to keep talent (brutal dilution when the stock recovers) or hold the line and watch people leave.
Nothing in your brand gets used more often or for longer than your name. It shows up on every invoice, every pitch deck, and every email signature. Most founders still pick theirs by committee brainstorm or by whatever .com happens to be available. This post walks through what separates names that stick from names that get changed in two years, and why "safe" names that try to please everyone end up saying nothing at all.
Product teams compound every sprint, but marketing blows up and starts over every time a new CMO or agency walks in. The ICP gets rewritten, the positioning deck gets shelved, and four months of messaging work vanishes. AI makes it possible to finally close that gap. One marketing leader with the right setup can build a living system where content draws from validated frameworks, new hires inherit full strategic context on day one, and this quarter's learnings actually feed into next quarter's campaigns instead of being forgotten.
AI now initiates most sales conversations, shifting the buyer journey. Companies are reorganizing their go-to-market strategies to adapt to AI's influence, with marketing teams building tools in-house to drive growth. While AI handles small-value purchases autonomously, complex enterprise decisions remain human-led, necessitating equipping AI as effectively as internal teams.
Real-time database and backend built for AI-coded apps.
Conduct AI-led interviews and rank candidates based on performance instead of resumes.
Deploy and manage AI agents to analyze and trade on prediction markets.
Out of ~15,000 seed-funded startups per fund cycle, maybe one produces a $10B+ revenue company. The people who build those aren't a better version of great founders, they're a different species entirely. A 2 sigma founder builds a $10-100M business. A 3 sigma founder builds a unicorn. A 4 sigma founder doesn't adjust their ambition to match reality, they adjust reality. Each level is a 10x jump, not incremental.
For the last decade, progress in AI came from making a single model bigger and feeding it more data. This post argues that the next leap won't come from scaling further. Current models are already good enough. The real bottleneck is how we organize and coordinate them. Better systems wrapping existing models will outperform one monolithic model trained on everything, and the field is only starting to figure out what that looks like.
Building a feature has never been cheaper. Carrying one is just as expensive as it's ever been. Every addition makes the product harder to learn, harder to maintain, and harder to evolve, and products rarely fail for lack of features. They fail because they accumulated too many that nobody had the discipline to kill. When execution gets cheap, the right response isn't to ship more, it's to raise the bar for what gets to stay.
Enterprises making an AI architecture decision today are deciding who gets to own the state that builds around it.
By the time a startup needs a second product, it's perfectly set up to fail at building one.
Stop borrowing someone else's compass.
A non-technical founder went from a few college CS classes to shipping production features daily with AI coding tools, now ranked top 1% of engineers at small startups.
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You know social is contributing to pipeline -- but your CEO might need some help to understand how it works.
Local media advertising is harder to sell because the mix of local businesses has shifted. New advertisers favor targeted digital tactics over traditional media. The market added 1.7M net new businesses after the pandemic, and many of these buy search ads and targeted banners that sales reps often miss. There are now 3X more in-house marketers than ad sales reps, and these marketers focus on quick results and bottom-funnel tools rather than brand building. This makes advertisers more skeptical and more focused on proof over reach.
AI token costs are a fast-growing gap in 2026 marketing budgets because usage scales quickly and is hard to predict. Traditional B2B budget splits of about 45% headcount, 45% programs, and 10% technology are shifting as tech spend rises by 2% to 3% due to AI tools and data needs. Headcount is flat at many SaaS companies, but expectations are higher. AI-native companies are hiring as revenue grows faster than staff. Program spend is steady but faces pressure to lower cost-per-outcome with the help of AI. The biggest challenge is forecasting token usage for both internal AI work and vendor pricing, since even providers lack clear benchmarks.
Brands using purposeful animated GIFs in email see a 37:1 ROI, a 105% improvement over those relying on static images alone. Interactive elements like gamified reveals and video-GIFs drive 73% higher click-to-open rates by creating a curiosity gap that pulls subscribers into engagement. However, 31% of subscribers label animated emails as annoying when the motion feels random, so every animation needs to serve a clear function. Gmail clips emails over 102KB, and GIFs should stay under 500KB to render properly on mobile.
August built rapid growth by openly normalizing periods and using a community-first strategy that prioritizes relatability over traditional ads. It has grown to 400K TikTok followers and 187K Instagram followers by using founder-led content and high volume testing that attract and convert Gen Z audiences aged 16-29. It focuses on organic content first and uses paid ads only for retargeting. It scales reach through programs like campus advocates and partnerships with brands like DoorDash and USA Track and Field.
Results improve when brands move beyond large creator rosters and toward more deliberate strategic choices. Habit Burger drove 10.5M impressions by deploying food trucks and creators on the ground during Big Game weekend instead of buying a traditional ad spot. Tapatío Ramen integrated direct purchase links into creator content and ran paid amplification on the best-performing assets, achieving 41% higher ROAS and surpassing its own Amazon sales through Shopify. Totino's took a different route, giving TikTok gaming creators minimal creative guardrails and landing a $0.61 CPM across 11.7M impressions.
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Stop rage-clicking through GA4, HubSpot fields, and 20 open tabs. Adapt [13] is an AI computer that connects your marketing stack, autonomously learns your business, and gets smarter with every task. Spin up cross-tool dashboards, vibe code the UIs you've always wanted,
Gulrez Khan will dive into the world of data storytelling in this free event that will take place on April 20 at 9 AM PT. Learn how to transform raw data into compelling narratives that captivate your audience in a world of AI slop. He will cover how to design visuals that guide attention and reduce cognitive load.
AI tools excel at scaffolding generic UI components, migrating design tokens, and outlining features. However, they fall apart when front-end work requires pixel precision or bespoke interactions. The core issue is that LLMs trained on common web patterns cannot see rendered output, struggle with math-heavy layout calculations, and have no control over the chaotic browser environment where HTML and CSS actually execute. Scroll-driven animations and complex component states are particularly weak spots. The more complex a component becomes, the slower and less reliable AI assistance gets.
Nikon's ad proves reliability by placing its camera in a high-stakes context instead of making generic claims. Using NASA as an authority signal, it shows the camera was trusted on a Space Shuttle without a backup, signaling extreme dependability. Specific details like 1971 and the admission of modifications add credibility. The ad then connects this extreme use case to everyday users, implying the camera will handle normal situations with ease. It focuses on customer outcomes, making the message feel like a logical conclusion rather than a sales claim.
Treating AEO as an upgrade to SEO misunderstands how LLMs work. There are no keywords to rank for and no page one. Responses are personalized based on prompts, history, preferences, and platform. Visibility depends on brand authority and trust signals, not technical optimization alone, though crawlability and structured data still matter for ingestion. The takeaway is to measure brand recognition and third-party mentions, not prompt visibility like a ranking report.
ClickUp scaled thousands of AI-optimized listicles beyond its expertise, gaining AI visibility but losing millions in Google traffic.
Shared dislike unites people, and that unity turns casual buyers into vocal advocates.
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Unwrap's customer intelligence platform brings all your customer feedback (surveys, reviews, support tickets, social comments, ect.) into a single view, then uses AI + NLP to surface the most actionable insights and deliver them straight to your inbox.
Amazon CEO Andy Jassy recently published an annual shareholder letter that takes aim at several competitors, including Microsoft, Intel, Nvidia, Starlink, and OpenAI. Demand for Amazon's newest chip, Trainium 3, is so high that it is nearly sold out. AWS' homegrown CPU, Graviton, is now used expansively by 98% of the top 1,000 EC2 customers. Amazon Leo, scheduled to launch in mid-2026, is already succeeding. AWS plans to spend $200 billion on capex in 2026, mostly on building out data centers.
Tesla's new compact SUV will be priced substantially lower than the Model 3. It will measure about 14 feet in length and weigh approximately 1.5 metric tons. Tesla plans to use a smaller battery pack and a single electric motor. Production will be based in Shanghai, but it is unlikely to begin in 2026.
The changes from Apollo to Artemis represent a massive leap in software complexity. Today's software manages every thermal valve and power relay, making it a challenge to ensure the software remains synchronized and valid while under a barrage of cosmic radiation. This article discusses Orion's zero-tolerance architecture and how it offers a preview of a future where mainstream computing can achieve the same always-on resilience required for space.
β-Thalassaemia, a disease closely related to sickle-cell anemia, is caused by a large range of mutations, so it can't be fixed with a single editing system. Researchers took an approach that involved reactivating the fetal version of the gene, which has a higher affinity for oxygen than normal hemoglobin. Editing the gene in patient stem cells and transplanting them back resulted in the patients' hemoglobin levels rising. All patients in the trial saw side effects, but they met the trial's key success metric of going over six months without needing a transfusion to control their disease.
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Build customizable real-time AI humans [10] that see, listen, react, and remember, and deploy them in any app or website in minutes. Already powering Amazon, Mayo Clinic, and Salesforce in production. Tavus trains cutting-edge models that give interactive AI personas real-time perception, emotional intelligence, evolving memory, and natural conversation timing- sub-200ms, 42+ languages, one API.
This thread discusses programming hunches on where certain aspects of a problem could be better solved by another solution. These hunches often create room for interesting discussions or new ways to think about problems.
Ramp hit 99% adoption of AI tools across the company, but most people were stuck as they had no idea how to improve their setup. This made the company decide to build its own AI productivity suite to make every employee an AI power-user without the pain of having to configure their environment. Owning its AI infrastructure is a competitive advantage for Ramp. It helps the company learn faster, build better AI-native products, and deliver better outcomes for customers.
OpenAI plans to have 30 gigawatts of compute by 2030. Anthropic is expected to have roughly seven to eight gigawatts by the end of 2027. OpenAI recently sent a memo to investors saying that Anthropic was operating on a meaningfully smaller curve, characterizing the company as compute-constrained. Both Anthropic and OpenAI are gearing up for IPOs, potentially this year. They have to convince investors that they have sustainable business models that can withstand the pressure of going up against cash-rich rivals.
Many people used ChatGPT when it first came out and then stopped. These people's opinions of AI capabilities are outdated. People who pay for AI and use it professionally are the ones who see the frontier of the technology. This group is subject to the highest amount of 'AI Psychosis' because the recent improvements in these domains have been nothing short of staggering.
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Unwrap's customer intelligence platform [16] connects your entire organization to the true voice of the customer with AI-driven insights from all your feedback, delivered proactively to you.
There are now five ChatGPT subscription tiers: Free, Go, Plus, the $100 per month Pro level, and a $200 per month Pro tier.
The new agreement will run from 2027 to 2032.
It's relatively easy to cut underwater cables, and there's no foolproof way to defend them.
TLDR's Applied AI team is tasked with making every process at TLDR legible to code, runnable by anyone, and composable into larger workflows. Join a small, fast moving team using the latest AI tools with an unlimited token budget. Learn more [21].