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Announcing the latest release from Qodo: Modern engineering runs across hundreds of repos, dozens of teams, and AI that writes code faster than anyone can review it. Today, Qodo releases features built for these teams. - Cross Repo Review: Qodo now maps dependencies across your repos and catches breaking...

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632,227 views • 1 month ago •via X (Twitter)

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AI can write more code than any team can review by hand, and a pull request can look fine while hiding a security issue or missing a requirement. In our new short course, AI Code Review, built in collaboration with Qodo and taught by Nnenna 👩🏽‍💻✨, you'll learn the practices that make AI code review effective: review before you open a pull request, give the reviewer full context about your codebase, and triage findings by risk. Then you'll build your own review agent, from a context engine that finds the right code to a team of specialized reviewers. Enroll for free:
1:39

AI can write more code than any team can review by hand, and a pull request can look fine while hiding a security issue or missing a requirement. In our new short course, AI Code Review, built in collaboration with Qodo and taught by Nnenna 👩🏽‍💻✨, you'll learn the practices that make AI code review effective: review before you open a pull request, give the reviewer full context about your codebase, and triage findings by risk. Then you'll build your own review agent, from a context engine that finds the right code to a team of specialized reviewers. Enroll for free:

DeepLearning.AI

53,674 views • 16 days ago

2025 is the year of the CLI, not 𝘥𝘦𝘴𝘱𝘪𝘵𝘦 its simplicity but 𝘣𝘦𝘤𝘢𝘶𝘴𝘦 of it. I’m thrilled to share today’s announcement. It’s a milestone for Qodo’s mission, and for every team that wants to put AI to work across the SDLC. Let’s dive in! 𝐌𝐞𝐞𝐭 𝐐𝐨𝐝𝐨 𝐆𝐞𝐧 𝐂𝐋𝐈 Built for creating, running, and managing AI agents that automate critical development tasks across the SDLC, from analyzing CI tests and logs to triaging production errors and more. 𝐊𝐞𝐲 𝐟𝐞𝐚𝐭𝐮𝐫𝐞𝐬 ✅ Runs everywhere ✅ Designed for task execution, not just Q&A ✅ Trigger from CI, hooks, or cloud functions—you choose ✅ Works with all major models ✅ Fully customizable agents with their own tools, credentials, and goals And yes, it plays nicely with our other Qodo agents too (e.g., Qodo Merge, the AI code-review tool). This isn’t just about writing code—it’s about catching bugs earlier, automating deeper, and shipping higher-quality software. 𝐓𝐫𝐲 𝐢𝐭 𝐭𝐨𝐝𝐚𝐲 𝐢𝐧 𝐬𝐞𝐜𝐨𝐧𝐝𝐬 Make sure you have npm installed, then run: npm install -g @qodo/gen Oh, one more thing- 𝐄𝐱𝐩𝐥𝐨𝐫𝐞 𝐨𝐮𝐫 𝐥𝐢𝐛𝐫𝐚𝐫𝐲 𝐨𝐟 𝐬𝐩𝐞𝐜𝐢𝐚𝐥𝐢𝐳𝐞𝐝 𝐐𝐨𝐝𝐨 𝐀𝐠𝐞𝐧𝐭𝐬, and contribute your own so others can benefit!
3:13

2025 is the year of the CLI, not 𝘥𝘦𝘴𝘱𝘪𝘵𝘦 its simplicity but 𝘣𝘦𝘤𝘢𝘶𝘴𝘦 of it. I’m thrilled to share today’s announcement. It’s a milestone for Qodo’s mission, and for every team that wants to put AI to work across the SDLC. Let’s dive in! 𝐌𝐞𝐞𝐭 𝐐𝐨𝐝𝐨 𝐆𝐞𝐧 𝐂𝐋𝐈 Built for creating, running, and managing AI agents that automate critical development tasks across the SDLC, from analyzing CI tests and logs to triaging production errors and more. 𝐊𝐞𝐲 𝐟𝐞𝐚𝐭𝐮𝐫𝐞𝐬 ✅ Runs everywhere ✅ Designed for task execution, not just Q&A ✅ Trigger from CI, hooks, or cloud functions—you choose ✅ Works with all major models ✅ Fully customizable agents with their own tools, credentials, and goals And yes, it plays nicely with our other Qodo agents too (e.g., Qodo Merge, the AI code-review tool). This isn’t just about writing code—it’s about catching bugs earlier, automating deeper, and shipping higher-quality software. 𝐓𝐫𝐲 𝐢𝐭 𝐭𝐨𝐝𝐚𝐲 𝐢𝐧 𝐬𝐞𝐜𝐨𝐧𝐝𝐬 Make sure you have npm installed, then run: npm install -g @qodo/gen Oh, one more thing- 𝐄𝐱𝐩𝐥𝐨𝐫𝐞 𝐨𝐮𝐫 𝐥𝐢𝐛𝐫𝐚𝐫𝐲 𝐨𝐟 𝐬𝐩𝐞𝐜𝐢𝐚𝐥𝐢𝐳𝐞𝐝 𝐐𝐨𝐝𝐨 𝐀𝐠𝐞𝐧𝐭𝐬, and contribute your own so others can benefit!

Itamar Friedman

11,802 views • 1 year ago

Today I am excited to announce Greptile’s BIGGEST update ever 💥 Greptile is the AI code review bot loved by over 3,000 software teams across the world. We’re launching four things today: 🧠 Long-term memory for the code review agent so it can intelligently learn your company's idiosyncrasies and best practices 🤝 Ability to connect your Jira, Google Docs, and Notion via MCP so Greptile can have more context 🎯 Ability to create highly scoped rules that only apply to certain repos and file patterns 💰 A simpler, more predictable pricing model of flat $30/month/developer. Let’s dive in 🧵
0:59

Today I am excited to announce Greptile’s BIGGEST update ever 💥 Greptile is the AI code review bot loved by over 3,000 software teams across the world. We’re launching four things today: 🧠 Long-term memory for the code review agent so it can intelligently learn your company's idiosyncrasies and best practices 🤝 Ability to connect your Jira, Google Docs, and Notion via MCP so Greptile can have more context 🎯 Ability to create highly scoped rules that only apply to certain repos and file patterns 💰 A simpler, more predictable pricing model of flat $30/month/developer. Let’s dive in 🧵

Daksh Gupta

246,046 views • 1 year ago

A DEVELOPER FOUND SEVEN WAYS TO TAKE DOWN A PRODUCTION DATABASE THAT ALL LOOK EXACTLY LIKE NORMAL, INNOCENT CODE AND ALMOST EVERY TEAM IS SHIPPING AT LEAST ONE OF THEM RIGHT NOW 17 minutes from Josh Berkus, one of the people who actually maintains PostgreSQL, walking through the quiet mistakes that turn a healthy database into a 3am outage. -> The moment it lands, you realize none of these are exotic attacks. They're ordinary-looking decisions -- a query that locks a table, a connection that never closes, a setting no one ever questioned -- that work perfectly until the day they don't, and then they take everything down with them. The scary part isn't that the database breaks. It's how normal the code looks right up until it does. A query that runs in 5ms on your laptop and 5 minutes on prod. A migration that silently locks the whole table. A connection pool that runs dry the moment real traffic shows up. Every one of them passed review. Writing SQL that runs was never the hard part -> writing SQL that survives production is. And now that an AI agent is generating and firing queries at your real database faster than anyone can read them, every one of those seven landmines is one autocomplete away -- and the only person who can stop it is the one who already knows where they're buried. Your database doesn't go down because someone attacked it. It goes down because something that looked completely normal finally caught up with it. Save and Watch it today. You'll see the next outage coming before it lands ↓
16:51

A DEVELOPER FOUND SEVEN WAYS TO TAKE DOWN A PRODUCTION DATABASE THAT ALL LOOK EXACTLY LIKE NORMAL, INNOCENT CODE AND ALMOST EVERY TEAM IS SHIPPING AT LEAST ONE OF THEM RIGHT NOW 17 minutes from Josh Berkus, one of the people who actually maintains PostgreSQL, walking through the quiet mistakes that turn a healthy database into a 3am outage. -> The moment it lands, you realize none of these are exotic attacks. They're ordinary-looking decisions -- a query that locks a table, a connection that never closes, a setting no one ever questioned -- that work perfectly until the day they don't, and then they take everything down with them. The scary part isn't that the database breaks. It's how normal the code looks right up until it does. A query that runs in 5ms on your laptop and 5 minutes on prod. A migration that silently locks the whole table. A connection pool that runs dry the moment real traffic shows up. Every one of them passed review. Writing SQL that runs was never the hard part -> writing SQL that survives production is. And now that an AI agent is generating and firing queries at your real database faster than anyone can read them, every one of those seven landmines is one autocomplete away -- and the only person who can stop it is the one who already knows where they're buried. Your database doesn't go down because someone attacked it. It goes down because something that looked completely normal finally caught up with it. Save and Watch it today. You'll see the next outage coming before it lands ↓

slash1s

22,268 views • 2 months ago

The Clark County Election Department is introducing a new step in the in-person voting process in 2026. The check-in process and voting machines (now called ballot marking devices) will remain the same as in recent elections. But now, once a voter makes their selections, they get a printout of their ballot to review before submitting it through the new tabulators. This change aligns with statewide security standards and national best practices. By producing a paper ballot, every voter can easily review their selections before casting their vote. Paper records are then safely stored for audits and recounts if needed.
0:52

The Clark County Election Department is introducing a new step in the in-person voting process in 2026. The check-in process and voting machines (now called ballot marking devices) will remain the same as in recent elections. But now, once a voter makes their selections, they get a printout of their ballot to review before submitting it through the new tabulators. This change aligns with statewide security standards and national best practices. By producing a paper ballot, every voter can easily review their selections before casting their vote. Paper records are then safely stored for audits and recounts if needed.

Clark County Nevada

18,982 views • 7 months ago

Obsidian Bases are better than Notion databases. Most people never use them because they're too technical. This Claude skill changes this: Bases automatically aggregate notes from your vault into a structured table. I have dozens of bases; the one in the video replaces the reference manager, for instance. Problem: Most struggle with setting them up. There's now a skill built by one of the Obsidian developers that runs inside Claude. The skill scans your vault and automatically generates the base. The hardest Obsidian feature to learn. Solved with one sentence.
1:53

Obsidian Bases are better than Notion databases. Most people never use them because they're too technical. This Claude skill changes this: Bases automatically aggregate notes from your vault into a structured table. I have dozens of bases; the one in the video replaces the reference manager, for instance. Problem: Most struggle with setting them up. There's now a skill built by one of the Obsidian developers that runs inside Claude. The skill scans your vault and automatically generates the base. The hardest Obsidian feature to learn. Solved with one sentence.

Ilya Shabanov

34,406 views • 3 months ago

For those of you tired of yet another AI-based IDE: Check out Traycer. It's a Visual Studio Code extension (free forever for anyone working on open-source repositories), and it does something different from everyone else. You write a high-level prompt outlining the changes you want to make to your codebase, and the extension generates a plan for you before you touch the code. (It reminds me of GitHub Copilot Workspaces.) You can review the plan, understand exactly the changes the extension will make to your code, and make any modifications. When you are ready, you execute the plan, and Traycer stages all of the changes so you have a new opportunity to review everything. Only then does Traycer apply the changes. This is ideal for large codebases where you'd rather study the changes the AI plans to make before they make it into your code. Look at the video I recorded to see how it works. Thanks to the Traycer team for collaborating with me on this post!
7:27

For those of you tired of yet another AI-based IDE: Check out Traycer. It's a Visual Studio Code extension (free forever for anyone working on open-source repositories), and it does something different from everyone else. You write a high-level prompt outlining the changes you want to make to your codebase, and the extension generates a plan for you before you touch the code. (It reminds me of GitHub Copilot Workspaces.) You can review the plan, understand exactly the changes the extension will make to your code, and make any modifications. When you are ready, you execute the plan, and Traycer stages all of the changes so you have a new opportunity to review everything. Only then does Traycer apply the changes. This is ideal for large codebases where you'd rather study the changes the AI plans to make before they make it into your code. Look at the video I recorded to see how it works. Thanks to the Traycer team for collaborating with me on this post!

Santiago

84,497 views • 1 year ago

A DEVELOPER WHO HELPS MAINTAIN GIT ITSELF PROVED THAT USING GIT AND CONTRIBUTING WITH GIT ARE TWO DIFFERENT SKILLS AND ALMOST NO ONE LEARNS THE SECOND 55 minutes from Johannes Schindelin, one of git's most prolific contributors, on the workflow that gets your code accepted into a project you don't own. -> The moment it lands, committing to your own repo turns out to be the easy 10%. Landing a change in someone else's codebase is the part nobody taught you. Most people can save their own work. Far fewer can revise a patch five times, keep the history clean, and merge it into a project moving under them. Saving your code was never the skill -> getting it merged into someone else's is. And when an AI agent fires off a pull request nobody asked for, the person who knows how to really contribute is the one who gets it accepted instead of closed. Anyone can commit to their own repo. The person who can land a change in a project they don't own is the one who actually ships. Bookmark & Watch it today ↓
55:43

A DEVELOPER WHO HELPS MAINTAIN GIT ITSELF PROVED THAT USING GIT AND CONTRIBUTING WITH GIT ARE TWO DIFFERENT SKILLS AND ALMOST NO ONE LEARNS THE SECOND 55 minutes from Johannes Schindelin, one of git's most prolific contributors, on the workflow that gets your code accepted into a project you don't own. -> The moment it lands, committing to your own repo turns out to be the easy 10%. Landing a change in someone else's codebase is the part nobody taught you. Most people can save their own work. Far fewer can revise a patch five times, keep the history clean, and merge it into a project moving under them. Saving your code was never the skill -> getting it merged into someone else's is. And when an AI agent fires off a pull request nobody asked for, the person who knows how to really contribute is the one who gets it accepted instead of closed. Anyone can commit to their own repo. The person who can land a change in a project they don't own is the one who actually ships. Bookmark & Watch it today ↓

slash1s

12,750 views • 2 months ago

Your Plan SUCKS! 👀 Inspired by the legend Andrej Karpathy himself, I created a new skill: LLM Council. Create better plans, by committee. Supports: > Codex CLI > Gemini CLI > Claude Code > OpenCode Call the skill with your feature that you'd like to build. It will ask you some clarifying question, and then launch up to four parallel planning agents to create a detailed plan. Once the plans are in, all plans are anonymized and the "The Judge" will critique and choose the best plan *OR* the best parts from all of them. Finally, it will output a final-plan, which you can review and refine. I even created a nice UI for you to review and refine your plans. This Skill has been tested on Linux only, but should work on other platforms. Please report any bugs! Links in the comments.
0:28

Your Plan SUCKS! 👀 Inspired by the legend Andrej Karpathy himself, I created a new skill: LLM Council. Create better plans, by committee. Supports: > Codex CLI > Gemini CLI > Claude Code > OpenCode Call the skill with your feature that you'd like to build. It will ask you some clarifying question, and then launch up to four parallel planning agents to create a detailed plan. Once the plans are in, all plans are anonymized and the "The Judge" will critique and choose the best plan *OR* the best parts from all of them. Finally, it will output a final-plan, which you can review and refine. I even created a nice UI for you to review and refine your plans. This Skill has been tested on Linux only, but should work on other platforms. Please report any bugs! Links in the comments.

am.will

69,010 views • 6 months ago

#1 skill for developers in 2026: Automate everything you can using AI. I bet my lunch your team is dealing with all of these: • Stale documentation • Outdated dependencies • Poor test coverage • Deprecated APIs Every company I work with has these same problems. You can solve all of these right now. Automatically. Using AI. Here are 3 examples. Watch the attached video: I'm using Ona Automations to tackle this. These are background agents that run in the cloud, in a fully configured dev environment with your toolchain, your dependencies, and your services. You can run an unlimited number of these agents in parallel and across all your repositories. Claude Code and Codex only run locally, so they are hard to scale, and you can't run them when your computer is closed. Ona runs in the cloud. Here are the three examples: 1. Test coverage Run a nightly automation to identify any untested code paths, generate candidate tests, verify they pass, and open draft PRs. You wake up every morning to PRs that improve your test coverage. 2. Dependency upgrades Configure a weekly automation that bumps a dependency version, runs your full test suite, and reports any regressions. If everything is clean, it opens a PR. If something breaks, it opens a report so you can decide what to do. 3. Documentation auditing Set up a weekly automation that checks recent commits against your README file and setup guides, identifies broken examples and outdated instructions, and opens a PR with fixes.
5:18

#1 skill for developers in 2026: Automate everything you can using AI. I bet my lunch your team is dealing with all of these: • Stale documentation • Outdated dependencies • Poor test coverage • Deprecated APIs Every company I work with has these same problems. You can solve all of these right now. Automatically. Using AI. Here are 3 examples. Watch the attached video: I'm using Ona Automations to tackle this. These are background agents that run in the cloud, in a fully configured dev environment with your toolchain, your dependencies, and your services. You can run an unlimited number of these agents in parallel and across all your repositories. Claude Code and Codex only run locally, so they are hard to scale, and you can't run them when your computer is closed. Ona runs in the cloud. Here are the three examples: 1. Test coverage Run a nightly automation to identify any untested code paths, generate candidate tests, verify they pass, and open draft PRs. You wake up every morning to PRs that improve your test coverage. 2. Dependency upgrades Configure a weekly automation that bumps a dependency version, runs your full test suite, and reports any regressions. If everything is clean, it opens a PR. If something breaks, it opens a report so you can decide what to do. 3. Documentation auditing Set up a weekly automation that checks recent commits against your README file and setup guides, identifies broken examples and outdated instructions, and opens a PR with fixes.

Santiago

25,804 views • 5 months ago

Today we are introducing 2 key features to JAIGP: AI Review & Open Prompting. AI review is part of 5-step process where papers get feedback from & are evaluated based on their ability to address that feedback. This means they are not stuck in an endless AI review loop. Open prompting is a newer idea. We are making all prompts we used to create JAIGP open, and also, opening up the journal's rules to the community for suggestions. You can suggest the prompt we should run next! So, if you have opinions about AI generated papers, you can share them directly with us at
2:20

Today we are introducing 2 key features to JAIGP: AI Review & Open Prompting. AI review is part of 5-step process where papers get feedback from & are evaluated based on their ability to address that feedback. This means they are not stuck in an endless AI review loop. Open prompting is a newer idea. We are making all prompts we used to create JAIGP open, and also, opening up the journal's rules to the community for suggestions. You can suggest the prompt we should run next! So, if you have opinions about AI generated papers, you can share them directly with us at

César A. Hidalgo

13,453 views • 4 months ago

AI is changing the software engineering craft. Anders Hejlsberg (Anders Hejlsberg) - creator of C#, TypeScript and industry legend - on why code review needs to get more enjoyable in response: #1 - AI is shifting the craft from writing code, to reviewing code: "In a sense, we're all turning into project managers. We can have an army of junior programmers, called agents, that will just spit out reams of code but someone's got to have the big picture and review all of that. And so, increasingly, our craft is going from one of writing the code, to one of reviewing the code and building the architecture of the code and overseeing the work. It's a different kind of craft. It's a different kind of enjoyment. I've always liked writing the code. To me that was the fulfilling part, seeing it work. In a way, AI robs a little bit of that, because I am less interested in reviewing code." #2 - The code review experience should be improved: "I think we could also make the process of reviewing code much more interesting than it is today. I mean, today, you see a list of diffs in alphabetical order and now it's up to you to make heads or tails of it. There are more pedagogical ways of presenting that. And you could have commentary generated by the AI that tells you what the changes are and whatever, and then tries to guide you along. So that symbiotic relationship, I think we need to work on that more and to keep the enjoyment in there."
2:04

AI is changing the software engineering craft. Anders Hejlsberg (Anders Hejlsberg) - creator of C#, TypeScript and industry legend - on why code review needs to get more enjoyable in response: #1 - AI is shifting the craft from writing code, to reviewing code: "In a sense, we're all turning into project managers. We can have an army of junior programmers, called agents, that will just spit out reams of code but someone's got to have the big picture and review all of that. And so, increasingly, our craft is going from one of writing the code, to one of reviewing the code and building the architecture of the code and overseeing the work. It's a different kind of craft. It's a different kind of enjoyment. I've always liked writing the code. To me that was the fulfilling part, seeing it work. In a way, AI robs a little bit of that, because I am less interested in reviewing code." #2 - The code review experience should be improved: "I think we could also make the process of reviewing code much more interesting than it is today. I mean, today, you see a list of diffs in alphabetical order and now it's up to you to make heads or tails of it. There are more pedagogical ways of presenting that. And you could have commentary generated by the AI that tells you what the changes are and whatever, and then tries to guide you along. So that symbiotic relationship, I think we need to work on that more and to keep the enjoyment in there."

The Pragmatic Engineer

39,011 views • 2 months ago

Your engineering team is about to snap. And your AI coding agent is making it worse. Introducing CodeRabbit Agent for Slack 🎉 A second brain for engineering teams. Because your tribal knowledge lives in Slack threads nobody can find. At CodeRabbit, we review millions of PRs every week and know how ace engineering teams operate on the planet. The same three things slow every team we see: - Context and decisions that live elsewhere. - Lack of a team-level durable knowledge base - And a trust layer that gives a safety net to your teams Built for Agentic SDLC workflows, CodeRabbit Agent for Slack solves all three problems in one shot while enabling teams to collaborate in real-time with the Agent. CodeRabbit Agent is Slack-native and builds your team’s operating context from every thread, every decision, every conversation your team has ever had. Make your team’s context compound!
1:37

Your engineering team is about to snap. And your AI coding agent is making it worse. Introducing CodeRabbit Agent for Slack 🎉 A second brain for engineering teams. Because your tribal knowledge lives in Slack threads nobody can find. At CodeRabbit, we review millions of PRs every week and know how ace engineering teams operate on the planet. The same three things slow every team we see: - Context and decisions that live elsewhere. - Lack of a team-level durable knowledge base - And a trust layer that gives a safety net to your teams Built for Agentic SDLC workflows, CodeRabbit Agent for Slack solves all three problems in one shot while enabling teams to collaborate in real-time with the Agent. CodeRabbit Agent is Slack-native and builds your team’s operating context from every thread, every decision, every conversation your team has ever had. Make your team’s context compound!

Harjot Gill

4,775,791 views • 3 months ago

Insane: 90-95% of Claude Code is now written by Claude Code. I asked Mike Krieger (Anthropic's CPO) what changes when so much of your code is written by AI? His answer: The bottleneck moves from coding/design to: 1. Upstream: aligning the team around what to build. 2. Downstream: the PR merge queue, reviewing and merging all of the new code into production. Anthropic is where other product teams will be in a year or two, so it's important we pay attention. "I would expect that a year from now, the way that we are conceiving of building and shipping software changes a lot. Because it's gonna be very painful to do it the current way."
0:36

Insane: 90-95% of Claude Code is now written by Claude Code. I asked Mike Krieger (Anthropic's CPO) what changes when so much of your code is written by AI? His answer: The bottleneck moves from coding/design to: 1. Upstream: aligning the team around what to build. 2. Downstream: the PR merge queue, reviewing and merging all of the new code into production. Anthropic is where other product teams will be in a year or two, so it's important we pay attention. "I would expect that a year from now, the way that we are conceiving of building and shipping software changes a lot. Because it's gonna be very painful to do it the current way."

Lenny Rachitsky

369,920 views • 1 year ago

I tested a new coding assistant on 34,568 lines of code. (Cursor could not achieve what this tool did.) AI models are improving every day: - Faster inference - Longer context lengths But are AI coding assistants advancing at the same rate? Can they truly understand your entire project? I tested Augment Code on my ai-engineering repo with ~35k lines of code. Augment Code is a powerful AI Assistant built for developers working with large, evolving codebases—needing an assistant that understands the full context of their projects. In the video demo below, I asked it to: - Merge two projects. - Answer a global context question. Here’s what happened: - It understood dependencies across both projects. - It merged them intelligently—without breaking anything. - It even created a README file for the merged project. - It answered my global query instantly, pulling from the full codebase. This is the difference between AI autocomplete and an AI engineer that truly understands your repo. Augment Code is powerful because it indexes your entire repo upfront. This way, it can answer questions instantly, no matter how large your project is. Lastly, Augment Code is fully compatible with VSCode, JetBrains, Vim, Slack, and more. Try them now, I have shared a link in the next tweet! Thanks to Augment Code for showing me their powerful AI coding assistant and working with me on this post.
0:31

I tested a new coding assistant on 34,568 lines of code. (Cursor could not achieve what this tool did.) AI models are improving every day: - Faster inference - Longer context lengths But are AI coding assistants advancing at the same rate? Can they truly understand your entire project? I tested Augment Code on my ai-engineering repo with ~35k lines of code. Augment Code is a powerful AI Assistant built for developers working with large, evolving codebases—needing an assistant that understands the full context of their projects. In the video demo below, I asked it to: - Merge two projects. - Answer a global context question. Here’s what happened: - It understood dependencies across both projects. - It merged them intelligently—without breaking anything. - It even created a README file for the merged project. - It answered my global query instantly, pulling from the full codebase. This is the difference between AI autocomplete and an AI engineer that truly understands your repo. Augment Code is powerful because it indexes your entire repo upfront. This way, it can answer questions instantly, no matter how large your project is. Lastly, Augment Code is fully compatible with VSCode, JetBrains, Vim, Slack, and more. Try them now, I have shared a link in the next tweet! Thanks to Augment Code for showing me their powerful AI coding assistant and working with me on this post.

Akshay 🚀

30,856 views • 1 year ago

"These are people that would never otherwise even touch code and they're now using Replit ⠕ themselves and they're building things that actually work." - Databricks CEO Ali Ghodsi Ali says non-technical employees across their 10,000 person company are building real software with Replit ⠕: "We have 5-6,000 people that are sitting in marketing or in HR or in finance and they don't have those technical skills. Replit is excellent. They love it." "Whenever you build a piece of software that works with Replit, it uses a database behind the scenes and actually we have a very deep partnership with them. So it uses our lakehouse offering behind the scenes." "Replit is really amazing for democratization to the broader masses. The people that you would never imagine would write a single line of code." Databricks Ventures also invested in Replit as of today.
1:10

"These are people that would never otherwise even touch code and they're now using Replit ⠕ themselves and they're building things that actually work." - Databricks CEO Ali Ghodsi Ali says non-technical employees across their 10,000 person company are building real software with Replit ⠕: "We have 5-6,000 people that are sitting in marketing or in HR or in finance and they don't have those technical skills. Replit is excellent. They love it." "Whenever you build a piece of software that works with Replit, it uses a database behind the scenes and actually we have a very deep partnership with them. So it uses our lakehouse offering behind the scenes." "Replit is really amazing for democratization to the broader masses. The people that you would never imagine would write a single line of code." Databricks Ventures also invested in Replit as of today.

Manny Bernabe

11,993 views • 5 months ago

lee from cursor JUST showed me the future of coding in this 29 min tutorial. what if instead of thinking about coding as you sitting alone in front of a screen , you started to think about it as you and a swarm of agents each taking on very specific roles, each showing up exactly when you need them, each running in the background without ever needing a coffee break. imagine a bugbot that doesn’t just review your pull request but triages the issue instantly, leaves intelligent comments, and in some cases even proposes a fix before a teammate has had the chance to read the description. imagine a security agent that quietly scans your code for vulnerabilities and flags them long before they become liabilities. imagine an agent dedicated to software culture, enforcing the little rules your team has decided matter, whether that’s never skipping loading states, banning certain lazy marketing phrases in your code comments, or making sure tests are always included. lee walked me through how all of this already exists inside cursor. he spun up custom commands like /code-review or /security-review that behave less like toys and more like trusted coworkers who have internalized the way your team thinks about quality. and when he combined that with rails, type safety, linters, formatting rules, test suites.... the agents learned to check themselves against the rails and fix mistakes automatically. you end up with a system where the codebase begins to feel alive, constantly watching itself, constantly healing. another thing i learned from lee was watching how he doesn’t pile everything into one giant context and hope for magic. he creates new chats for every new task, sometimes twenty or more in a single day, each one a fresh brain that isn’t bogged down by yesterday’s details. it’s a discipline that keeps the agents sharp and the outputs clean. pro tip, thanks Lee Robinson. this was a cool glimpse into the future of coding and some tips on how to squeeze the most out of cursor. anything is possible.
29:41

lee from cursor JUST showed me the future of coding in this 29 min tutorial. what if instead of thinking about coding as you sitting alone in front of a screen , you started to think about it as you and a swarm of agents each taking on very specific roles, each showing up exactly when you need them, each running in the background without ever needing a coffee break. imagine a bugbot that doesn’t just review your pull request but triages the issue instantly, leaves intelligent comments, and in some cases even proposes a fix before a teammate has had the chance to read the description. imagine a security agent that quietly scans your code for vulnerabilities and flags them long before they become liabilities. imagine an agent dedicated to software culture, enforcing the little rules your team has decided matter, whether that’s never skipping loading states, banning certain lazy marketing phrases in your code comments, or making sure tests are always included. lee walked me through how all of this already exists inside cursor. he spun up custom commands like /code-review or /security-review that behave less like toys and more like trusted coworkers who have internalized the way your team thinks about quality. and when he combined that with rails, type safety, linters, formatting rules, test suites.... the agents learned to check themselves against the rails and fix mistakes automatically. you end up with a system where the codebase begins to feel alive, constantly watching itself, constantly healing. another thing i learned from lee was watching how he doesn’t pile everything into one giant context and hope for magic. he creates new chats for every new task, sometimes twenty or more in a single day, each one a fresh brain that isn’t bogged down by yesterday’s details. it’s a discipline that keeps the agents sharp and the outputs clean. pro tip, thanks Lee Robinson. this was a cool glimpse into the future of coding and some tips on how to squeeze the most out of cursor. anything is possible.

GREG ISENBERG

47,160 views • 11 months ago

Nobody is writing 90% of their code using AI. Here's the uncomfortable truth: The real productivity gain from using AI to write code is closer to 10%, nowhere near the 90% people claim. Sundar Pichai said in 2024 that 30% of the new code at Google was AI-generated. However, he went on to admit, during Lex Friedman's podcast, that engineering velocity had only increased by about 10%. AI-generated code isn't free code. It still has to be reviewed, tested, and made production-ready. Optimizing a single step (code generation) doesn't boost output if bottlenecks shift elsewhere (code reviews). It doesn't matter how much code you generate if you can't keep up the review process. The solution: Automate as much as you can the review and verification of your code. I'm working with Sonar, who is sponsoring this post, and they will take care of the code quality and security analysis of your code: • They review over 300B lines of code every single day • They cover reliability, security, and maintainability for your code • You can integrate them into your CI/CD pipeline • You can install them in your IDE (I use their VSCode extension) • Support for more than 30 languages Here is a link so you can check them out:
4:42

Nobody is writing 90% of their code using AI. Here's the uncomfortable truth: The real productivity gain from using AI to write code is closer to 10%, nowhere near the 90% people claim. Sundar Pichai said in 2024 that 30% of the new code at Google was AI-generated. However, he went on to admit, during Lex Friedman's podcast, that engineering velocity had only increased by about 10%. AI-generated code isn't free code. It still has to be reviewed, tested, and made production-ready. Optimizing a single step (code generation) doesn't boost output if bottlenecks shift elsewhere (code reviews). It doesn't matter how much code you generate if you can't keep up the review process. The solution: Automate as much as you can the review and verification of your code. I'm working with Sonar, who is sponsoring this post, and they will take care of the code quality and security analysis of your code: • They review over 300B lines of code every single day • They cover reliability, security, and maintainability for your code • You can integrate them into your CI/CD pipeline • You can install them in your IDE (I use their VSCode extension) • Support for more than 30 languages Here is a link so you can check them out:

Santiago

296,570 views • 10 months ago

"We're trying to hire as many new grads as we can. We're finding that a lot of young people are agentmaxxing." - Replit founder Amjad Masad Amjad says that despite the “doom and gloom” around entry-level jobs, this is actually a “golden era” for young people who know how to use AI tools: "The roles are collapsing. We have designers shipping code, engineers shipping design, and salespeople shipping code." "The particular skill is not the bottleneck anymore. It is how ambitious you are, how generative you are, how creative you are, and how good you are at utilizing these tools." "So I think it's really a golden era for a lot of young people coming up right now in the job market. In our time, you would go and interview and become a Java J2EE software engineer doing global migrations. That was your title, right? It's no longer the case. We just want people to come and do things, and I think that's incredibly exciting."
1:49

"We're trying to hire as many new grads as we can. We're finding that a lot of young people are agentmaxxing." - Replit founder Amjad Masad Amjad says that despite the “doom and gloom” around entry-level jobs, this is actually a “golden era” for young people who know how to use AI tools: "The roles are collapsing. We have designers shipping code, engineers shipping design, and salespeople shipping code." "The particular skill is not the bottleneck anymore. It is how ambitious you are, how generative you are, how creative you are, and how good you are at utilizing these tools." "So I think it's really a golden era for a lot of young people coming up right now in the job market. In our time, you would go and interview and become a Java J2EE software engineer doing global migrations. That was your title, right? It's no longer the case. We just want people to come and do things, and I think that's incredibly exciting."

TBPN

47,144 views • 5 months ago