Luanne Misquitta

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

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Add 100k installs for productivity apps in less than 90 days through 4 steps: 1. Master the Efficiency Transformation Hook Productivity apps die because they focus on features instead of transformational outcomes Here’s some winning hooks for productivity apps that focus on outcomes > Time-Saving: "Wdym there’s an app for this??? > Organization: "Um guys, if you’re [doing this], just listen to me..." > Specific Use: "One [relatable situation] and I just found this???" 2. Create These 4 Content Types Only Stop making random content when you can just use these proven formats > Quick Workflow Demos (15-30 seconds showing specific productivity hacks) > Before/After Organization (visual chaos to organized transformation) > Day-in-the-Life (real users integrating app into daily routines) > Pain Point Solutions (specific productivity problems solved) 3. Deploy Creator Teams Specifically Recruit 4-6 micro-creators targeting specific avatars > students > entrepreneurs > busy parents > remote workers Test different hooks with each creator to identify winning angles for each demographic. 4. Use Psychology-Driven CTAs Generic CTAs kill conversions. Focus on using CTAs that are specific to your audience: > "Name of the app is XYZ btw" > "I found him on XYZ app btw" > "The app that saved my life was ABC in case you wanted to know" > Focus on dream outcomes, not features Instead of showcasing ALL features, Demonstrate ONE specific use case that solved your dream customer's time, efficiency, and productivity problems.

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How Stripe prevents double payments (explained in 2 mins or less): 1 They use idempotent APIs to prevent double payment. 2 They use UUID as the idempotency key. 3 They send the idempotency key through HTTP headers. 4 They create a new idempotency key whenever the request payload changes. 5 They store the idempotency keys in a database on the server side. 6 They query the database with keys to check if a request was processed earlier. 7 They process a request only if it's new and then store its idempotency key in the database. 8 They roll back a transaction using the ACID database on a server error. 9 They remove the idempotency keys from the database after 24 hours for reuse. 10 They use exponential back-off with jitter to avoid the thundering herd problem. What else would you add? —— 👋 PS - If you enjoyed this post, you'll love my detailed article about this case study (with visuals): → https://lnkd.in/erMkqwq4 ——— ♻ Repost to help others learn idempotent APIs. 👤 Follow Neo Kim for more.

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Stripe is building a way for developers to completely automate billing for tokens: 1/ pick model (e.g. openai o3, claude sonnet 4, gemini 1.5 flash, etc) 2/ set margin (e.g. charge +10% over inference) 3/ add two lines of code to your app It works great with subscription models too: charge X dollars per month for N tokens, then, after that, pass along token cost with a Y% markup on overage. “Intelligence-based pricing” Want to try it? [email protected]

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𝗢𝗽𝗲𝗻𝗔𝗜’s Product Leader just revealed the playbook for 𝗔𝗜 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 and why it decides who wins and who fails 🤯 Chegg lost 90 percent of its value because it waited too long. Jasper raised at a $1.2B valuation but collapsed under costs and commoditization. Duolingo lost trust and users by forcing AI in without strategy. AI does not forgive bad decisions. Features get copied in weeks. Costs spiral faster than revenue. The market punishes hesitation brutally. That is why Miqdad Jaffer, 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗟𝗲𝗮𝗱 at 𝗢𝗽𝗲𝗻𝗔𝗜, created a step by step guide to AI product strategy https://lnkd.in/dn2SCSUb Inside you will find: ▪️ The brutal economics of inference costs and why SaaS style pricing breaks in AI ▪️ The only three moats that matter in AI which are data, distribution, and trust ▪️ The 4D Framework that stress tests your direction, differentiation, design, and deployment ▪️ Pricing and positioning models that protect you from death by API wrapper Most AI startups fail not because of technology, but because of their strategy. 🎁I am sharing the full guide and frameworks here: https://lnkd.in/dn2SCSUb

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It’s a bit surprising to see people in my feed this morning say OpenAI’s AgentKit “kills” a bunch of construction tech startups. It's incomplete and doesn't capture the whole picture. Yes, generic, horizontal-wrapper startups that just sit on top of foundation models are in trouble. But industry AI agents built for very specific, complex workflows, who understand and care about their customers are going to thrive as AI adoption accelerates. I played with Agent Kit yesterday for a simple task and here’s my take: 1. Agent kits are impressive but shallow without depth. To make them truly useful, you still need a quasi-technical team to build, monitor, and maintain them. In industries like construction, where margins are razor-thin and overhead has to stay lean, that’s a tough sell. Most GCs and subcontractors aren’t about to staff up a new AI department while trying to keep projects on budget. 2. Vertical Industries are highly complex and for good reason.  In construction for example: Permitting, liability coverage, inspections, safety, pay schedule and regulatory compliance all add layers of checks and balances. These workflows exist to protect businesses and keep projects safe and sustainably capitalized. You can make AgentKit work for you, you're just going to need people to do it.  And every construction company I have ever worked with wants to hire more builders and field professionals, experts in their domain, not more people in the back office. 3. Deep, domain specific challenges are where the real value is. The vertical winners won’t just bolt agents onto a foundation model, they’ll build depth with industry specific workflows. Nobody wants vendor lock-in, so they’ll be model agnostic, swapping in and out whichever LLM performs best for the task. They’ll integrate seamlessly with legacy players and every other tool the industry already depends on. They’ll own the ETL process, cleaning and structuring messy, fragmented data. They’ll capture proprietary workflows and the tribal knowledge unique to the vertical. And they’ll deliver enterprise grade privacy and transparency. 4. The organizational behaviors matter. In construction and many other verticals, middle managers often optimize for their resume and their next job while leadership optimizes for profit margins and retention. Sometimes it can be in direct tension of each other, one group wants experimental automation engineers, the other wants tools to drive productivity without new headcount. For example, software in general doesn't require additional health insurance. So yes, AgentKit and whatever comes next will eat the easy stuff like document search or report templates. But the real, hard, high value work will always go to the companies who know their vertical inside out and build for it.  Foundational models are democratizing access. Vertical AI is where the winners get created.  We’ve seen this in past technological shifts. 

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This breakdown of how 12+ companies use credit-based pricing in SaaS is a practical guide for modern monetization. It highlights four patterns, from pure-credit models like Clay to credit add-ons like Monday.com, and shows how to balance flexibility, transparency, and value capture in pricing. Worth reading if you are mapping AI or SaaS pricing strategies: https://lnkd.in/dJvtVsj3

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