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Sherlock AI is the security homebase for Web3 engineering teams. Pick a repo, branch, and commit, start a run, and get visibility into vulnerabilities and attack paths tied to the code you’re about to ship. The payoff is simple: it gives engineers time back. Less time doing manual review.... show more

SHERLOCK

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1,391,475 次观看 • 6 个月前 •via X (Twitter)

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Deeptrace (Deeptrace) is an AI SRE and debugging companion that automatically investigates every production alert and surfaces a clear root cause, so your engineers spend less time firefighting and more time building. Congrats on the launch, srinth & andy lee!

Deeptrace (Deeptrace) is an AI SRE and debugging companion that automatically investigates every production alert and surfaces a clear root cause, so your engineers spend less time firefighting and more time building. Congrats on the launch, srinth & andy lee!

Y Combinator

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AI designs functional hardware! 🔧 Claude Opus 4.7 is leading CAD benchmarks, and AI is starting to design functional hardware autonomously. The shift happening: CAD as a tool → CAD as an autonomous system. The model didn't just sketch geometry, it generated mechanically valid designs with joints and motion constraints. It optimized for real-world functionality, not just visual correctness like earlier LLMs. This mirrors what happened in coding last year. In software, engineers moved toward requirements, architecture, and orchestration while code became the compiled outcome of AI systems. Mechanical engineering may follow the same path: from designing parts → defining intent. This changes the role of engineering teams. Instead of manually designing every component, engineers will define constraints, validate outputs, and focus on system-level architecture. Engineers spend less time on geometry and more time on optimization and integration. AI handles the mechanical validity checks that traditionally required experienced designers. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

AI designs functional hardware! 🔧 Claude Opus 4.7 is leading CAD benchmarks, and AI is starting to design functional hardware autonomously. The shift happening: CAD as a tool → CAD as an autonomous system. The model didn't just sketch geometry, it generated mechanically valid designs with joints and motion constraints. It optimized for real-world functionality, not just visual correctness like earlier LLMs. This mirrors what happened in coding last year. In software, engineers moved toward requirements, architecture, and orchestration while code became the compiled outcome of AI systems. Mechanical engineering may follow the same path: from designing parts → defining intent. This changes the role of engineering teams. Instead of manually designing every component, engineers will define constraints, validate outputs, and focus on system-level architecture. Engineers spend less time on geometry and more time on optimization and integration. AI handles the mechanical validity checks that traditionally required experienced designers. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

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Today, we are proud to introduce Sherlock Audit Engine. Audit Engine brings the strongest available AI security approaches into one coordinated review. We’ve spent months building it in private and testing it against high-stakes code. A new era of AI-native security review starts now. Audit Engine is now live.

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𝗿𝗮𝗺𝗮𝗸𝗿𝘂𝘀𝗵𝗻𝗮— 𝗲/𝗮𝗰𝗰

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136,737 次观看 • 14 天前

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garry tan just open-sourced a 128k-star repo that basically turns claude code into your entire engineering org it’s called gstack: 23 tools that break a single claude workflow into product, engineering, qa, review, and release lanes > /office-hours turns a rough idea into a real product spec > /plan-ceo-review + /plan-eng-review try to tear the plan apart before any code ships > /review hunts for bugs and patches them > /qa spins up an actual browser and tests the app end to end > /ship runs the final checks and pushes the pr think → plan → build → review → test → ship → reflect garry says this setup let him ship 3 production services and 40+ features in 60 days while still running yc full-time one developer sets the compass. the repo runs the loop.

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I now spend more time building and far less time on outreach emails. Every campaign includes: • Finding prospects • Learning about each company • Personalizing every email • Writing follow-ups • Tracking replies That workflow can add up quickly—long before I even hit “Send.” So I tried Sintra. After I shared a bit of context about my business, it started generating personalized outreach sequences, follow-up emails, and messages tailored to different prospects—all in one place. Now, instead of starting from scratch, I review the drafts, make quick edits when needed, and send. The biggest difference isn’t just that AI helps write emails. It’s that I get my time back to focus on building while Sintra handles the repetitive parts. If outreach is a key part of your work, this is worth trying. 🔗 Use code: ZAYVEN75 for 75% off for lifetime.

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Its time Indian youth and parents give up on Engineering Graduation especially in IT AI will do a better job at building software. And to use AI to build software you do not need to go to the process of getting a Engineering Degree.

Its time Indian youth and parents give up on Engineering Graduation especially in IT AI will do a better job at building software. And to use AI to build software you do not need to go to the process of getting a Engineering Degree.

Woke Eminent

171,090 次观看 • 7 个月前

Jensen Huang just explained why every company cutting engineers over AI is asking the entirely wrong question. Huang: “People say, I don’t need software engineers because apparently coding is going to be automated.” That was the narrative. Here is what Huang actually did. Huang: “I’ve given AIs to every one of my software engineers and hardware engineers and engineers period. 100% of NVIDIA has AI assistants, AI coders, and they’re busier than ever.” Not fewer engineers. Not smaller teams. Busier than ever. That is the line most companies are getting completely wrong right now. They hear “AI can write code” and immediately start cutting headcount. Huang did the opposite. He armed everyone. Huang: “And so the question is, what is the task versus what is the job? No different than a financial analyst; the task is mess around with spreadsheets, but the job is to make financial advice. The job is to help a customer.” Writing code was always the task. It was never the job. The job is architecture. Knowing what to build. Why it matters. How it fits into a system that actually creates value. Code is the execution layer between the idea and the outcome. Nothing more. When you automate that layer, you don’t eliminate the engineer. You eliminate the bottleneck between what they can envision and what they can ship. The companies using AI to cut headcount are optimizing for cost. The companies using AI to multiply output are optimizing for territory. Nvidia chose territory. Every engineer at the most valuable semiconductor company on Earth now operates with an AI assistant. Not a pilot program. Not an experiment. Company-wide. Every function. Every team. And the result is not less work. It is more work. Faster. At a scale that was physically impossible twelve months ago. The companies that understand the difference between eliminating engineers and unleashing them will build what comes next. The ones that don’t will watch their best talent walk out the door to the ones that did.

Jensen Huang just explained why every company cutting engineers over AI is asking the entirely wrong question. Huang: “People say, I don’t need software engineers because apparently coding is going to be automated.” That was the narrative. Here is what Huang actually did. Huang: “I’ve given AIs to every one of my software engineers and hardware engineers and engineers period. 100% of NVIDIA has AI assistants, AI coders, and they’re busier than ever.” Not fewer engineers. Not smaller teams. Busier than ever. That is the line most companies are getting completely wrong right now. They hear “AI can write code” and immediately start cutting headcount. Huang did the opposite. He armed everyone. Huang: “And so the question is, what is the task versus what is the job? No different than a financial analyst; the task is mess around with spreadsheets, but the job is to make financial advice. The job is to help a customer.” Writing code was always the task. It was never the job. The job is architecture. Knowing what to build. Why it matters. How it fits into a system that actually creates value. Code is the execution layer between the idea and the outcome. Nothing more. When you automate that layer, you don’t eliminate the engineer. You eliminate the bottleneck between what they can envision and what they can ship. The companies using AI to cut headcount are optimizing for cost. The companies using AI to multiply output are optimizing for territory. Nvidia chose territory. Every engineer at the most valuable semiconductor company on Earth now operates with an AI assistant. Not a pilot program. Not an experiment. Company-wide. Every function. Every team. And the result is not less work. It is more work. Faster. At a scale that was physically impossible twelve months ago. The companies that understand the difference between eliminating engineers and unleashing them will build what comes next. The ones that don’t will watch their best talent walk out the door to the ones that did.

Dustin

82,810 次观看 • 5 个月前

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Avi Chawla

28,628 次观看 • 8 个月前

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.Autumn AI, Inc. is building the first real-time signal intelligence platform for GTM teams. Define your ICP and the signals that matter, and Autumn delivers a condensed, real-time feed filtered by intents. Congrats on the launch Vishnu Sampathkumar and Shiv Kampani!

Y Combinator

71,043 次观看 • 6 个月前

AI saves time, but the question is where that time actually goes. At Sundae Labs we put it into something most teams skip: proper documentation. 📜 That means going back through the code after AI writes it and making sure: - the reasoning behind each decision is documented - whoever reads it next understands not just what it does, but why That extra pass is what keeps the quality bar where it needs to be 👇

AI saves time, but the question is where that time actually goes. At Sundae Labs we put it into something most teams skip: proper documentation. 📜 That means going back through the code after AI writes it and making sure: - the reasoning behind each decision is documented - whoever reads it next understands not just what it does, but why That extra pass is what keeps the quality bar where it needs to be 👇

Sundae Labs 🍨

13,219 次观看 • 2 个月前

Building sophisticated systems in web3 usually takes dedicated teams, years of grinding and elite development skills to get to the peak of the industry. Well that era just ended... Agentic mode is live and it's time to turn it on!

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Eitherway

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Star Citizen

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