Taus Noor

BREAKING: 86.8% SWE-bench Verified, Biggest Leap Ever. "SWE Bench verified has been the Super Bowl of AI coding benchmarks for the past two years. The last time the coding benchmarks had a double digit leap was March of 2024. This ushered in a huge era for AI coding. The AI agent was here." -Brian Elliott We just proved the AI pundits wrong at Google's Sunnyvale campus. While everyone's saying "AI acceleration is plateauing**,** we took a different approach at Blitzy. The results? 86.8% on SWE-Bench verified - the industry's premier AI coding benchmark that tests systems on real-world GitHub issues - delivering a 10% lift over the current leader and the first double-digit leap since March 2024. Here's what changed the game: --> System 2 AI: Trading time for quality. Our platform spends hours mapping every line of code relationally and hierarchically summarizing entire codebases (100M+ lines). --> Multi-agent orchestration: Extended inference time compute with validation across multiple models with built-in prompt engineering. As I told the audience: "The next frontier of progress is not just pre-training models. By driving more inference time compute into your systems, you are able to drive up the quality of output dramatically." Link to white paper in comments. #blitzy #ainativesdlc #autonomoussoftwaredevelopment #aicoding #nextfrontier

262

39 Comments

The hottest role in AI startups right now isn’t Forward Deployed Engineers. It isn't GTM Engineers. It’s Deployment Strategists. Decagon calls it an “Agent Product Manager.” Harvey calls it a “Solutions Architect.” Palantir Technologies has had versions of this role for years. And the salaries are climbing fast: - Decagon: $200k–$285k - Palantir Technologies: $120k–$200k - Figma: $150k–$260k - Ramp: $100k–$180k - Harvey: $190k–$260k So who are these people? They are usually pseudo-technical -- CS or engineering majors, or folks with technical work experience. Many come from 2 years in consulting, IB, or PE, then jump into startups to get their hands dirty. They are young, hungry, polished, and comfortable being in front of customers. What do they actually do? They make sure enterprise AI deployments succeed. A $100k+ deal does not survive on a nice pitch or a self-serve onboarding flow. It survives if the customer sees value in the pilot. That means: - Embedding directly with the customer - Designing prompt logic for specific workflows - Working with engineering to align integrations and data flow - Helping exec teams define their AI roadmap - Running feedback loops into product and GTM Why does this role matter so much? Because enterprise AI is messy. Integrations, data transfer, and adoption make or break a deal. Most buyers are using AI for the first time, and each has unique workflows. Deployment Strategists bridge that gap. They own the outcome. They are accountable for making pilots successful, which often means millions in revenue down the line. At Conversion, Sam Bochner has been leading this work for us. We are now thinking about scaling it into a full team. Because a few successful pilots can fund an entire department, and the cost of failed deployments is too high to ignore. Is this just a rebrand of customer success? Not really. Success is about answering tickets and renewals. Deployment Strategy is about going deep with a few enterprise accounts, extracting maximum value, and ensuring the pilot closes into a multi-year contract. Call it Agent PM, Solutions Architect, or Deployment Strategist. Whatever the title, this is becoming one of the most important roles in AI SaaS.

641

90 Comments

Stripe is quietly building its own Layer 1 blockchain called Tempo with Paradigm Fortune spotted the project through a now-deleted job listing. Tempo is being built for one purpose: making stablecoin payments fast and easy. The team is small, just five people right now, and they are focused on high-speed transactions. Tempo will work with Ethereum’s smart contracts, so developers can use the tools they already know. This means they will not need to learn an entirely new system to start building. Stripe already owns Bridge for stablecoin infrastructure and Privy for wallet tech. Tempo gives them the last piece of the puzzle so they can control the entire payments stack from stablecoin creation to settlement on their own chain. With the new GENIUS Act creating clear rules for stablecoins in the US, the timing could not be better. Stripe seems ready to make stablecoin payments part of everyday business without relying on other networks. Bottom line: Stripe is not just joining crypto. It is aiming to own the rails stablecoins run on. Keeping an eye on this 👀

63

8 Comments

Oracle just joined NVIDIA, Google, and Airbnb in switching to front-loaded vesting. Is this the new standard? This is one of the biggest changes to tech compensation in decades. Here's what's happening: 1/ More companies are front-loading equity → Oracle, NVIDIA, Google, Airbnb, DoorDash, Uber, Pinterest → Instead of 25/25/25/25, it's now 40/30/20/10 or similar splits 2/ Why now? → Cuts long-term equity costs → Makes financials more attractive to investors → Gives candidates big Year 1 perceived value → Ties refreshers to performance 3/ What it means for you: → Don't just look at Year 1 ask about refresher grants → Compare steady-state vesting (Years 2–4), not just the front-load → Be ready for more performance-driven equity culture Front-loaded vesting is spreading fast across major tech companies. Expect mid-to-large tech companies to adopt it next. Would you rather have more equity upfront or stability spread evenly across 4 years?

88

11 Comments

Startup exploitation reaches peak absurdity when non-technical founders offer $60k SF salaries for "founding engineer" roles with 0.5% equity. This systematic labor exploitation targets technical workers through aspirational language while providing starvation wages in expensive markets. The founder manipulation playbook: - Use "founding" titles to justify below-market compensation - Offer minimal equity while demanding maximum technical responsibility - Exploit learning opportunity narratives to justify professional underpayment - Position technical work as privileged access rather than essential business contribution Your startup evaluation framework: - Does compensation reflect market rates or founder equity preservation strategies? - Are equity offerings proportional to responsibility levels or designed to minimize founder dilution? - Do learning opportunities justify below-market wages or serve founder cost optimization? - Are technical contributions valued appropriately or treated as discounted labor? The most expensive technical talent mistakes happen when skilled engineers accept equity theater instead of market-rate compensation. Non-technical founders benefit through reduced operational costs while technical employees subsidize business development through discounted labor. Demand market-rate compensation regardless of equity promises that typically provide minimal financial return. Choose companies that value technical expertise through appropriate compensation rather than founders who exploit professional development anxiety. Build career progression through market-rate positions that provide genuine equity participation proportional to technical contribution levels.

238

73 Comments