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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 views • 6 months ago •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

67,975 views • 9 months ago

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 →

Lukas Ziegler

25,458 views • 4 months ago

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.

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.

SHERLOCK

45,194 views • 6 days ago

Dumb loops beat clever workflows. A great talk about Ralph Loops. Build Dumb AI Loops That Ship by Chris Parsons. Most teams building with AI agents reach for multi-agent orchestration, planning graphs, and elaborate tool chains. Then they spend months debugging them. A single loop that processes one ticket at a time, evaluates its own output, and improves on the next run will outperform all of it.

Dumb loops beat clever workflows. A great talk about Ralph Loops. Build Dumb AI Loops That Ship by Chris Parsons. Most teams building with AI agents reach for multi-agent orchestration, planning graphs, and elaborate tool chains. Then they spend months debugging them. A single loop that processes one ticket at a time, evaluates its own output, and improves on the next run will outperform all of it.

𝗿𝗮𝗺𝗮𝗸𝗿𝘂𝘀𝗵𝗻𝗮— 𝗲/𝗮𝗰𝗰

23,908 views • 2 months ago

ANTHROPIC LEAKED A 4-AGENT SETUP THAT CUTS A CODEBASE AUDIT FROM 3 DAYS TO 20 MINUTES you point it at a repo and walk away - it comes back with what breaks, ranked, patches already tested. repo → map → 4 auditors → rank → fix → verify → report → back into the map the map cuts the repo by blast radius, not by folder - skip it and four agents audit the same three files and miss the one that ships broken. 4 auditors run in parallel with separate contexts - deps, secrets, dead code, hot paths, and none of them sees another's findings. rank is code, not an agent - sort by what breaks production, drop the duplicates, zero tokens. the fixer only opens patches for the top slice - a hundred findings nobody acts on is a report, not an audit. verify runs the suite on every patch and red goes back to the fixer - that patch only, never the whole batch. the back edge into the map is the whole trick - accepted findings become rules, so next week starts where this one ended. one human step in all of it: which fixes ship - 20 minutes instead of 3 days. save this and read the full graph engineering course below ↓

ANTHROPIC LEAKED A 4-AGENT SETUP THAT CUTS A CODEBASE AUDIT FROM 3 DAYS TO 20 MINUTES you point it at a repo and walk away - it comes back with what breaks, ranked, patches already tested. repo → map → 4 auditors → rank → fix → verify → report → back into the map the map cuts the repo by blast radius, not by folder - skip it and four agents audit the same three files and miss the one that ships broken. 4 auditors run in parallel with separate contexts - deps, secrets, dead code, hot paths, and none of them sees another's findings. rank is code, not an agent - sort by what breaks production, drop the duplicates, zero tokens. the fixer only opens patches for the top slice - a hundred findings nobody acts on is a report, not an audit. verify runs the suite on every patch and red goes back to the fixer - that patch only, never the whole batch. the back edge into the map is the whole trick - accepted findings become rules, so next week starts where this one ended. one human step in all of it: which fixes ship - 20 minutes instead of 3 days. save this and read the full graph engineering course below ↓

Hanako

136,346 views • 9 days ago

Even if AI will write 90% of code. But it won’t replace engineers. Windsurf’s founder explains that even if AI generates most of the code, engineers still spend time reviewing it, debugging issues, designing systems, testing, deploying, and making architectural decisions. Those parts don’t disappear. He brings up Amdahl’s Law to make the point: speeding up one part of the workflow doesn’t dramatically change the total time unless everything else speeds up too. For example, if writing code takes 30 out of 100 total time units, and AI brings it down to 3, the total time still only drops to 73. That’s just a 27% improvement. More productivity doesn’t mean fewer engineers. It just increases the ROI of building tech. And when ROI goes up, companies are more likely to build—and hire more people to do it. Take JPMorgan Chase. They have 50,000+ engineers and a $17B software budget. If each engineer can ship more software, that doesn’t reduce headcount. It raises the cost of not building. Companies like Windsurf and Anthropic are hiring more engineers—not less—despite being at the edge of AI code generation.

Even if AI will write 90% of code. But it won’t replace engineers. Windsurf’s founder explains that even if AI generates most of the code, engineers still spend time reviewing it, debugging issues, designing systems, testing, deploying, and making architectural decisions. Those parts don’t disappear. He brings up Amdahl’s Law to make the point: speeding up one part of the workflow doesn’t dramatically change the total time unless everything else speeds up too. For example, if writing code takes 30 out of 100 total time units, and AI brings it down to 3, the total time still only drops to 73. That’s just a 27% improvement. More productivity doesn’t mean fewer engineers. It just increases the ROI of building tech. And when ROI goes up, companies are more likely to build—and hire more people to do it. Take JPMorgan Chase. They have 50,000+ engineers and a $17B software budget. If each engineer can ship more software, that doesn’t reduce headcount. It raises the cost of not building. Companies like Windsurf and Anthropic are hiring more engineers—not less—despite being at the edge of AI code generation.

Aish

27,268 views • 1 year ago

Packaging design used to feel like a job for professional designers. Then I tried Packify AI. You can start with a simple product idea and turn it into a complete packaging concept through AI without spending hours in complicated design software. The interesting part is what happens after the first design: → Edit individual layers → Generate realistic AI product photos → Preview the package in 3D → Export production-ready dielines The whole workflow feels like: Idea → Design → Edit → Product photo → 3D preview → Dieline Less back-and-forth. Less technical setup. More time to focus on the actual product. A useful tool for designers, e-commerce sellers, and anyone building a physical product. Try it free: Use my code RIAZ20 for 20% off. Packify AI #aidesign #packifydesign #packifyai

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Md Riyazuddin

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Time is valuable. Slack AI gives you more of it. 🙌✨ And starting today, every Slack user on a paid plan can start getting more time back with AI that works right where they need it to (in Slack). Learn more at:

Time is valuable. Slack AI gives you more of it. 🙌✨ And starting today, every Slack user on a paid plan can start getting more time back with AI that works right where they need it to (in Slack). Learn more at:

Slack

24,860 views • 2 years ago

Teams are cooking some code. 🧑‍💻 Smart Contracts, APIs, frontend, AI agents. It is possible to develop products on chain under 24 hours. Complete products. Especially with the age of AI, everything is faster, and anyone with some prompting skills and an idea can start building. The night will be long. Time to build more!

Teams are cooking some code. 🧑‍💻 Smart Contracts, APIs, frontend, AI agents. It is possible to develop products on chain under 24 hours. Complete products. Especially with the age of AI, everything is faster, and anyone with some prompting skills and an idea can start building. The night will be long. Time to build more!

Robert Sasu | dev/acc

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There’s growing discussion around Qubic’s AI, AIGarth, within the dev and scientific teams. Keep a close eye on this new GitHub repo. Updates may drop at any time. After all, this is what Qubic was built for⏬

There’s growing discussion around Qubic’s AI, AIGarth, within the dev and scientific teams. Keep a close eye on this new GitHub repo. Updates may drop at any time. After all, this is what Qubic was built for⏬

Qubic

24,125 views • 1 year ago

Interestingly, as we have AI agents that run in the background, the speed of AI becomes incrementally less important than the core underlying capability level. When you could only give AI small bits of work, then the speed of response mattered a ton. The rate at which you can go back and forth with AI in real-time was the determining factor on how useful it is. But as AI agents can perform more complex and useful tasks in parallel behind the scenes, you can much more easily afford to wait longer for work to get done assuming it’s valuable. Now the focus becomes more on how you review and orchestrate the agents’ work. And the main factor is just how useful and usable is the output that the agent came back with.

Interestingly, as we have AI agents that run in the background, the speed of AI becomes incrementally less important than the core underlying capability level. When you could only give AI small bits of work, then the speed of response mattered a ton. The rate at which you can go back and forth with AI in real-time was the determining factor on how useful it is. But as AI agents can perform more complex and useful tasks in parallel behind the scenes, you can much more easily afford to wait longer for work to get done assuming it’s valuable. Now the focus becomes more on how you review and orchestrate the agents’ work. And the main factor is just how useful and usable is the output that the agent came back with.

Aaron Levie

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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.

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.

ZAYVEN KNOX

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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 views • 7 months ago

An MCP server that detects production-grade code quality issues in real-time! Even though AI is now generating code at light speed, the engineering bottleneck has just moved from writing to reviewing, and now devs spend 90% of their debugging time on AI-generated code. AI reviewers aren't that reliable either because they share the same fundamental blind spots as AI generators do: - They pattern match, not proof check. - They validate syntax, not system behavior. - They review code, not consequences. I have been using the SonarQube MCP Server (by Sonar) to solve this. It produces enterprise-grade code analysis and returns instant feedback on bugs, vulnerabilities, and code smells right where you're working (Claude Code, Cursor, etc.). Its capabilities have emerged from the 750B+ lines of code SonarQube processes daily, so it has seen every bug pattern that exists. This includes: - Security vulnerabilities (SQL injection, XSS, hardcoded secrets) - Code smells and technical debt - Test coverage gaps - Maintainability issues The setup is simple: - Install the SonarQube MCP server - Add it to your AI assistant's config Done! SonarQube is now part of your AI coding workflow. I have shared the GitHub repo in the replies! P.S. Thanks to Sonar for working with me today.

An MCP server that detects production-grade code quality issues in real-time! Even though AI is now generating code at light speed, the engineering bottleneck has just moved from writing to reviewing, and now devs spend 90% of their debugging time on AI-generated code. AI reviewers aren't that reliable either because they share the same fundamental blind spots as AI generators do: - They pattern match, not proof check. - They validate syntax, not system behavior. - They review code, not consequences. I have been using the SonarQube MCP Server (by Sonar) to solve this. It produces enterprise-grade code analysis and returns instant feedback on bugs, vulnerabilities, and code smells right where you're working (Claude Code, Cursor, etc.). Its capabilities have emerged from the 750B+ lines of code SonarQube processes daily, so it has seen every bug pattern that exists. This includes: - Security vulnerabilities (SQL injection, XSS, hardcoded secrets) - Code smells and technical debt - Test coverage gaps - Maintainability issues The setup is simple: - Install the SonarQube MCP server - Add it to your AI assistant's config Done! SonarQube is now part of your AI coding workflow. I have shared the GitHub repo in the replies! P.S. Thanks to Sonar for working with me today.

Avi Chawla

28,628 views • 8 months ago

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

82,757 views • 5 months ago

.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!

.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 views • 6 months ago

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 views • 1 month ago

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!

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!

Eitherway

220,567 views • 2 months ago

To ensure you’re ready to take on the bloodthirsty gangs and merciless outlaws, every flyable ship and drivable ground vehicle released last year is available again for a limited time. In Case You Missed It:

To ensure you’re ready to take on the bloodthirsty gangs and merciless outlaws, every flyable ship and drivable ground vehicle released last year is available again for a limited time. In Case You Missed It:

Star Citizen

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This is more than hype - this is utility, delivered. We’re entering the AI Era - and Spectre AI is building the on-chain infrastructure to guide it. $SPECT powers a growing suite of real-time tools built for traders, builders, and analysts navigating crypto’s next chapter. What is Spectre AI? Watch the video to find out! This is the next wave of $SPECT. With the AI Research Platform now live, the powerful AI Screener is on the horizon - unlocking the next level of real-time insight, discovery, and on-chain clarity. #ai

This is more than hype - this is utility, delivered. We’re entering the AI Era - and Spectre AI is building the on-chain infrastructure to guide it. $SPECT powers a growing suite of real-time tools built for traders, builders, and analysts navigating crypto’s next chapter. What is Spectre AI? Watch the video to find out! This is the next wave of $SPECT. With the AI Research Platform now live, the powerful AI Screener is on the horizon - unlocking the next level of real-time insight, discovery, and on-chain clarity. #ai

SPECTRE AI

227,791 views • 1 year ago