AI Empowered Devs

AI Empowered Devs From Dev To AI-Native Dev •
Practical AI Workflows • Real Projects • Peer Support

08/05/2026

💎 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁 𝟳 - What if your agent doesn't need more context - just smarter searching? (Webinar 28.04.26)
𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴: stop dumping everything into the agent's brain.

📌 Here's the next short highlight from the recent webinar with Milko Slavov - one idea at a time, so we can actually dig into each one. 👇

𝗪𝗼𝗿𝗸𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗹𝗼𝘁𝘀 𝗼𝗳 𝗳𝗶𝗹𝗲𝘀 𝗶𝗻 𝗮𝗻 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁 𝗶𝘀 𝗵𝗮𝗿𝗱𝗲𝗿 𝘁𝗵𝗮𝗻 𝗶𝘁 𝗹𝗼𝗼𝗸𝘀.
→Paste too much - you hit context limits.
→Paste too little - the agent misses the point.

In the webinar, someone asked Milko how he manages this. His answer had two parts:

𝗣𝗮𝗿𝘁 𝗼𝗻𝗲: agents search before they read.
Instead of loading whole files, they search first, find the relevant slice, then read only what matters.

𝗣𝗮𝗿𝘁 𝘁𝘄𝗼 (𝘁𝗵𝗲 𝗯𝗶𝗴𝗴𝗲𝗿 𝗺𝗼𝘃𝗲):
When the agent has to do something recurring - like building their weekly status report from dozens of markdown files - Milko doesn't have it re-read everything every time.

𝗛𝗲 𝗵𝗮𝘀 𝘁𝗵𝗲 𝗮𝗴𝗲𝗻𝘁 𝗰𝗼𝗱𝗶𝗳𝘆 𝘁𝗵𝗲 𝘄𝗼𝗿𝗸 𝗶𝗻𝘁𝗼 𝗮 𝘀𝗰𝗿𝗶𝗽𝘁.
Then the agent just runs the script. One tool call. No file reads.

𝗧𝗵𝗲 𝗿𝗲𝘀𝘂𝗹𝘁?
"I'm on the Max 20 subscription. It's very hard to hit limits."

𝗧𝗵𝗲 𝘀𝗵𝗶𝗳𝘁 𝗶𝗻 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴:
Context engineering isn't about giving your agent more.
It's about giving it the right tools so it asks for less.

💬 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻:
How do you manage context and do you hit the limit often?

💎 𝗧𝗵𝗶𝘀 𝗶𝘀 𝗳𝗿𝗼𝗺 𝗼𝘂𝗿 𝟮𝟴 𝗔𝗽𝗿𝗶𝗹 𝘄𝗲𝗯𝗶𝗻𝗮𝗿: 𝗔𝗜-𝗡𝗮𝘁𝗶𝘃𝗲 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 - How Small Teams Ship Production Software with AI Agents, with Milko Slavov (founder of Bugzy AI).

𝗧𝗵𝗲 𝗳𝘂𝗹𝗹 𝘀𝗲𝘀𝘀𝗶𝗼𝗻 𝗰𝗼𝘃𝗲𝗿𝘀 𝗵𝗶𝘀 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 - from /capture and /groom slash commands to autonomous GitHub Actions, preview deployments and how they handle quality, docs and security.

𝗪𝗮𝘁𝗰𝗵 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝗿𝗲𝗰𝗼𝗿𝗱𝗶𝗻𝗴 𝗶𝗻𝘀𝗶𝗱𝗲 𝘁𝗵𝗲 𝗔𝗜-𝗣𝗼𝘄𝗲𝗿𝗲𝗱 𝗗𝗲𝘃𝘀 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆 - a place where devs share what's actually working (and what isn't) with AI agents in real teams.
👉 https://join.aiempowered.dev/

07/05/2026

💎𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁 𝟲 - What if your docs stayed current without anyone remembering to update them? (from Webinar 28.04.26)

📌 Here’s the next short highlight from the recent webinar with Milko Slavov 👇

𝗢𝗻𝗲 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗶𝗱𝗲𝗮 𝗳𝗿𝗼𝗺 𝗠𝗶𝗹𝗸𝗼’𝘀 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄:
Don’t treat documentation as something you “do later.”
Make it part of the development pipeline.

In Bugzy.ai, they use two steps:

𝗦𝘁𝗲𝗽 𝟭 - 𝗕𝗲𝗳𝗼𝗿𝗲 𝗰𝗼𝗱𝗶𝗻𝗴

When grooming a task, their /plan command asks the agent to check documentation impact.

→What internal docs need to change?
→What public docs need to change?

That happens before code is written.

𝗦𝘁𝗲𝗽 𝟮 - 𝗔𝗳𝘁𝗲𝗿 𝘀𝗵𝗶𝗽𝗽𝗶𝗻𝗴

A separate weekly workflow scans the commits, drafts changelog updates and opens a PR for human review.

→So docs don’t depend on someone remembering.
→They are part of the workflow.

𝗧𝗵𝗲 𝗯𝗶𝗴𝗴𝗲𝗿 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆:

Docs usually rot when they are treated as a separate task.
If they are part of planning, PRs or release flow, they have a much better chance of staying alive.

💬 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻:

Are docs part of your dev workflow or something you update “later”?

P.S. Watch the full recording inside the AI Empowered Devs Community - a place where devs share what's actually working (and what isn't) with AI agents in real teams.
👉 https://join.aiempowered.dev/

06/05/2026

💎𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁 𝟱 - Drop Notion. Drop Jira. Use the repo. (from Webinar 28.04.26)
What if your small team doesn't need a ticket system at all?

📌Here's the next short highlight from the recent webinar with Milko Slavov. 👇
__________________________________
When Milko started Bugzy.ai, he used Notion with MCP for task tracking. Solo founder, personal workspace, worked great. Claude even moved tickets between columns automatically.

𝗧𝗵𝗲𝗻 𝗵𝗲 𝗵𝗶𝗿𝗲𝗱 𝗵𝗶𝘀 𝗳𝗶𝗿𝘀𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿. 𝗧𝗵𝗮𝘁'𝘀 𝘄𝗵𝗲𝗻 𝘁𝗵𝗲𝘆 𝗵𝗶𝘁 𝗮 𝗳𝗼𝗿𝗸:
Move Notion to a shared workspace? Or rethink whether they needed Notion at all?

𝗧𝗵𝗲𝘆 𝗽𝗶𝗰𝗸𝗲𝗱 𝘁𝗵𝗲 𝘀𝗲𝗰𝗼𝗻𝗱.

Their reasoning was simple:
"𝗟𝗟𝗠𝘀 𝗮𝗿𝗲 𝗴𝗿𝗲𝗮𝘁 𝗮𝘁 𝘄𝗼𝗿𝗸𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗳𝗶𝗹𝗲𝘀. 𝗪𝗵𝘆 𝗱𝗼 𝘄𝗲 𝗲𝘃𝗲𝗻 𝗻𝗲𝗲𝗱 𝗮𝗻 𝗲𝘅𝘁𝗲𝗿𝗻𝗮𝗹 𝘀𝘆𝘀𝘁𝗲𝗺?"

For a 2-person team, the answer was: they didn't.

Tasks, epics, priorities - all moved into repo files. The whole task system became markdown the agent reads, writes, and updates.

Milko was direct - they could do this BECAUSE they were small. No external stakeholders. No PMs to keep aligned. No reporting layers to maintain.

For small AI-first teams, an external task tracker isn't always a feature.
Sometimes it's just one more system to maintain.

𝗧𝗵𝗲 𝗯𝗶𝗴𝗴𝗲𝗿 𝗹𝗲𝘀𝘀𝗼𝗻:
Don't copy the workflow of a 200-person company when you're 2 people.
________________________________________
💬 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻
For a small AI-first team, would you move tasks into the repo, or would you still keep Jira/Linear/Notion?
If you do it differently, what’s your process?

P.S. Watch the full recording inside the AI Empowered Devs Community - a place where devs share what's actually working (and what isn't) with AI agents in real teams.
👉 https://join.aiempowered.dev/

05/05/2026

💎Highlight 4 - 𝗪𝗵𝗮𝘁 𝗶𝗳 𝗔𝗜 𝗱𝗶𝗱𝗻'𝘁 𝘄𝗿𝗶𝘁𝗲 𝘆𝗼𝘂𝗿 𝘁𝗲𝗰𝗵 𝗱𝗲𝗯𝘁 - 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 𝗱𝗶𝗱? (from Webinar 28.04.26)

📌Here's the next short highlight from the recent webinar with Milko Slavov. 👇
________________________________________
𝗪𝗵𝗲𝗻 𝗻𝗲𝘄 𝘁𝗲𝗮𝗺 𝗺𝗲𝗺𝗯𝗲𝗿𝘀 𝗷𝗼𝗶𝗻𝗲𝗱 𝗠𝗶𝗹𝗸𝗼'𝘀 𝘁𝗲𝗮𝗺, 𝘀𝗼𝗺𝗲𝗼𝗻𝗲 𝘀𝗮𝗶𝗱 𝘁𝗵𝗲 𝗾𝘂𝗶𝗲𝘁 𝗽𝗮𝗿𝘁 𝗼𝘂𝘁 𝗹𝗼𝘂𝗱:

"𝗬𝗼𝘂 𝗰𝗮𝗻 𝘁𝗲𝗹𝗹 𝗔𝗜 𝘄𝗿𝗼𝘁𝗲 𝗺𝗼𝘀𝘁 𝗼𝗳 𝘁𝗵𝗶𝘀 𝗰𝗼𝗱𝗲."
He didn't argue. The quality wasn't great.

𝗧𝗵𝗲 𝗳𝗶𝘅 𝘄𝗮𝘀𝗻'𝘁 "𝘂𝘀𝗲 𝗮 𝗯𝗲𝘁𝘁𝗲𝗿 𝗺𝗼𝗱𝗲𝗹."
It was "add a step."

After grooming, before implementation, 𝘁𝗵𝗲𝘆 𝗮𝗱𝗱𝗲𝗱 𝗮 𝗰𝗼𝗱𝗲-𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝘀𝘁𝗲𝗽. It inspects every file the agent will touch and decides what unit tests need to be added, what linting rules apply, what verification belongs in the plan.

𝗗𝗶𝗱 𝗶𝘁 𝗳𝗶𝘅 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴?
No. Milko was honest: "It improved coverage. We can do better."

𝗕𝘂𝘁 𝘁𝗵𝗲 𝗹𝗲𝘀𝘀𝗼𝗻 𝗵𝗼𝗹𝗱𝘀:
AI doesn't create tech debt. Your weak process does.
If your process is solid, AI ships faster.
If your process is sloppy, AI ships slop faster.
________________________________________
💬 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻
If you 10x'd your team's throughput tomorrow, where would AI most expose your weak spots?
→ Code reviews
→ Testing
→ Architecture decisions
→ Honestly, all three
Other?
Curious what would crack first.

💎 𝗧𝗵𝗶𝘀 𝗶𝘀 𝗳𝗿𝗼𝗺 𝗼𝘂𝗿 𝟮𝟴 𝗔𝗽𝗿𝗶𝗹 𝘄𝗲𝗯𝗶𝗻𝗮𝗿: 𝗔𝗜-𝗡𝗮𝘁𝗶𝘃𝗲 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 - How Small Teams Ship Production Software with AI Agents, with Milko Slavov (founder of Bugzy AI).

𝗧𝗵𝗲 𝗳𝘂𝗹𝗹 𝘀𝗲𝘀𝘀𝗶𝗼𝗻 𝗰𝗼𝘃𝗲𝗿𝘀 𝗵𝗶𝘀 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 - from /capture and /groom slash commands to autonomous GitHub Actions, preview deployments and how they handle quality, docs and security.

𝗪𝗮𝘁𝗰𝗵 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝗿𝗲𝗰𝗼𝗿𝗱𝗶𝗻𝗴 𝗶𝗻𝘀𝗶𝗱𝗲 𝘁𝗵𝗲 𝗔𝗜-𝗣𝗼𝘄𝗲𝗿𝗲𝗱 𝗗𝗲𝘃𝘀 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆 - a place where devs share what's actually working (and what isn't) with AI agents in real teams.

👉 https://join.aiempowered.dev/

04/05/2026

💎Highlight 3 - Anatomy of a well-groomed task (from Webinar 28.04.26)
What if your agent isn't bad at coding - it's just bad at guessing what you actually want?
📌Here's the next short highlight from the recent webinar with Milko Slavov. 👇
________________________________________
Most teams write tickets like:
"Update the dashboard to show project status."
Then they're surprised when the agent botches the implementation.

𝗠𝗶𝗹𝗸𝗼'𝘀 /𝗴𝗿𝗼𝗼𝗺-𝘁𝗮𝘀𝗸 𝗰𝗼𝗺𝗺𝗮𝗻𝗱 𝗮𝗹𝘄𝗮𝘆𝘀 𝗼𝘂𝘁𝗽𝘂𝘁𝘀 𝘁𝗵𝗲 𝘀𝗮𝗺𝗲 𝘀𝗶𝘅 𝗳𝗶𝗲𝗹𝗱𝘀:
• Description
• Acceptance criteria
• User stories
• Edge cases
• Related files
• Verification steps

That's not a coincidence. It's the minimum signal an agent needs to plan well.
Skip half of these and the agent fills the gaps with assumptions. Usually wrong ones.
The agent didn't create this problem. It just made it visible - and made it cost you.
________________________________________
💬 𝗤𝘂𝗶𝗰𝗸 𝗴𝘂𝘁-𝗰𝗵𝗲𝗰𝗸 - 𝘄𝗵𝗶𝗰𝗵 𝗼𝗻𝗲 𝗮𝗿𝗲 𝘆𝗼𝘂?
→ You skip most of these and write 1-2 line tickets
→ You do most, skip one or two
→ You have your own template with even more fields
Curious, what's your process for writing a ticket the AI can actually execute?

Watch full recording in the community portal 👉 https://join.aiempowered.dev/

02/05/2026

💎 Highlight 2 - Plan mode is the actual unlock (Webinar 28.04.26)
👉 full recording - https://join.aiempowered.dev/

📌 Here's the next short highlight from the recent webinar with Milko Slavov - one idea at a time, so we can actually dig into each one. 👇
________________________________________
Milko uses three escalating levels of planning - and they exist for a reason.
Level 1 - built-in plan mode. Great for exploring, but generic.
Level 2 - a custom /plan slash command, more specific to their workflow.
Level 3 - full high-level + low-level architecture docs. He spent 2 days writing them for the Bugzy 2.0 refactor.

Why three levels?
Because for the Bugzy 2.0 rewrite - a major architectural change - they tried planning it with their normal /plan command. It produced garbage. The task was simply too big for any plan that didn't start with proper architecture docs first.
That's how Level 3 was born: 1,000+ lines of high-level design, then low-level docs for specific decisions, then 5-10 actual implementation tasks underneath.

The bigger point: planning isn't one thing. It scales with complexity. The 5 extra minutes (or 2 days, for the big stuff) is a trade he'll take every time.
________________________________________
💬 Question:
How does planning fit into your AI workflow today - do you skip it, use plan mode or run your own custom setup? And if you've ever had a plan produce complete garbage on a big change, what did you do about it?

🎥 This is from our 28 April webinar: AI-Native Development - How Small Teams Ship Production Software with AI Agents, with Milko Slavov (founder of Bugsy AI).
Watch the full recording inside the AI-Powered Devs Community - a place where devs share what's actually working (and what isn't) with AI agents in real teams.
👉 full recording - https://join.aiempowered.dev/

02/05/2026

💎 Highlight 2 - Plan mode is the actual unlock (Webinar 28.04.26)
📌 Here's the next short highlight from the recent webinar with Milko Slavov - one idea at a time, so we can actually dig into each one. 👇
________________________________________
Milko uses three escalating levels of planning - and they exist for a reason.

𝗟𝗲𝘃𝗲𝗹 𝟭 - built-in plan mode. Great for exploring, but generic.
𝗟𝗲𝘃𝗲𝗹 𝟮 - a custom /plan slash command, more specific to their workflow.
𝗟𝗲𝘃𝗲𝗹 𝟯 - full high-level + low-level architecture docs. He spent 2 days writing them for the Bugsy 2.0 refactor.

𝗪𝗵𝘆 𝘁𝗵𝗿𝗲𝗲 𝗹𝗲𝘃𝗲𝗹𝘀?
Because for the Bugsy 2.0 rewrite - a major architectural change - they tried planning it with their normal /plan command. It produced garbage. The task was simply too big for any plan that didn't start with proper architecture docs first.
That's how Level 3 was born: 1,000+ lines of high-level design, then low-level docs for specific decisions, then 5-10 actual implementation tasks underneath.

𝗧𝗵𝗲 𝗯𝗶𝗴𝗴𝗲𝗿 𝗽𝗼𝗶𝗻𝘁: planning isn't one thing. It scales with complexity. The 5 extra minutes (or 2 days, for the big stuff) is a trade he'll take every time.
________________________________________
💬 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻:
How does planning fit into your AI workflow today - do you skip it, use plan mode or run your own custom setup? And if you've ever had a plan produce complete garbage on a big change, what did you do about it?

🎥 This is from our 28 April webinar: AI-Native Development - How Small Teams Ship Production Software with AI Agents, with Milko Slavov (founder of Bugsy AI).

Watch the full recording inside the AI-Powered Devs Community - a place where devs share what's actually working (and what isn't) with AI in real teams.
👉 https://join.aiempowered.dev/

01/05/2026

💎Highlight 1 - AI-first as default (from Webinar 28.04.26)
What if 'AI-first' isn't the future - it's just how some devs already ship?
📌We're breaking the recent webinar with Milko Slavov into short highlights. Here's the first one 👇
________________________________________
Milko founded Bugsy AI ~14 months ago.
He hasn't written code by hand since then.
Same for finance, admin, recruiting and other areas of his business - all done with AI.
This isn't a side project. 5 paying customers run on that code.
For some devs already running production businesses, AI-first isn't aspirational - it's just how they work.
________________________________________
💬 Question
When was the last time you wrote a meaningful chunk of code by hand?
And what's still holding you back from going fully AI-first?
Drop a comment - whether you're already there or barely started, what's the part that's hardest to give up to AI?

💎 This is from our 28 April webinar: AI-Native Development — How Small Teams Ship Production Software with AI Agents, with Milko Slavov (founder of Bugsy AI).
The full session covers his complete workflow
Watch the full recording inside the AI-Powered Devs Community - a place where devs share what's actually working (and what isn't) with AI agents in real teams.
👉 https://join.aiempowered.dev/ to watch the full recording

01/05/2026

💎Highlight 1 - AI-first as default (from Webinar 28.04.26)
What if 'AI-first' isn't the future - it's just how some devs already ship?
📌We're breaking the recent webinar with Milko Slavov into short highlights. Here's the first one 👇
________________________________________
Milko founded Bugsy AI ~14 months ago.
He hasn't written code by hand since then.
Same for finance, admin, recruiting and other areas of his business - all done with AI.
This isn't a side project. 5 paying customers run on that code.
For some devs already running production businesses, AI-first isn't aspirational - it's just how they work.
________________________________________
💬 Question
When was the last time you wrote a meaningful chunk of code by hand?
And what's still holding you back from going fully AI-first?
Drop a comment - whether you're already there or barely started, what's the part that's hardest to give up to AI?

💎 This is from our 28 April webinar: AI-Native Development — How Small Teams Ship Production Software with AI Agents, with Milko Slavov (founder of Bugsy AI).
The full session covers his complete workflow - from /capture and /groom slash commands to autonomous GitHub Actions, preview deployments, and how they handle quality, docs and security.
Watch the full recording inside the AI-Powered Devs Community - a place where devs share what's actually working (and what isn't) with AI agents in real teams.
👉 https://join.aiempowered.dev/

💎𝗪𝗲𝗯𝗶𝗻𝗮𝗿 𝗥𝗲𝗰𝗮𝗽: AI-Native Development - How Small Teams Ship Production Software with AI Agents.𝗥𝗲𝗰𝗼𝗿𝗱𝗶𝗻𝗴 𝗨𝗽𝗹𝗼𝗮𝗱𝗲𝗱/𝟮𝟴.𝟬𝟰...
30/04/2026

💎𝗪𝗲𝗯𝗶𝗻𝗮𝗿 𝗥𝗲𝗰𝗮𝗽: AI-Native Development - How Small Teams Ship Production Software with AI Agents.
𝗥𝗲𝗰𝗼𝗿𝗱𝗶𝗻𝗴 𝗨𝗽𝗹𝗼𝗮𝗱𝗲𝗱/𝟮𝟴.𝟬𝟰.𝟮𝟲/ 𝗶𝗻 𝘁𝗵𝗲 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆 - join to watch - https://join.aiempowered.dev/

Milko Slavov shared a real behind-the-scenes look at how his team uses AI agents to build production software.

This was not a polished demo where everything magically worked.

It was more useful than that.

Milko showed how his team moved from simple AI-assisted coding to a structured AI-native workflow with planning, task grooming, GitHub workflows, preview deployments, QA agents, documentation updates and continuous process improvement.

𝟳 𝗸𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀:

𝟭. 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 𝗮𝗿𝗲 𝗻𝗼𝘁 𝗺𝗮𝗴𝗶𝗰

The real value comes from the process around the agent.

AI-native development is not about writing one prompt and hoping the software builds itself. It is about creating a repeatable system.

𝟮. 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 𝗯𝗲𝗳𝗼𝗿𝗲 𝗰𝗼𝗱𝗶𝗻𝗴 𝗶𝘀 𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗹𝗲𝘃𝗲𝗿

Planning may add a few minutes, but it reduces the chance that AI goes in the wrong direction.

𝟯. 𝗕𝗲𝘁𝘁𝗲𝗿 𝘁𝗮𝘀𝗸 𝗴𝗿𝗼𝗼𝗺𝗶𝗻𝗴 𝗹𝗲𝗮𝗱𝘀 𝘁𝗼 𝗯𝗲𝘁𝘁𝗲𝗿 𝗔𝗜 𝗼𝘂𝘁𝗽𝘂𝘁

A vague idea can be expanded into a clearer task with description, acceptance criteria, edge cases, related files and verification steps.

The clearer the task, the better the output.

𝟰. 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝘄𝗼𝗿𝗸 𝗻𝗲𝗲𝗱𝘀 𝘃𝗲𝗿𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻

For something to be truly autonomous, AI should not only change the code.

It should also be able to test and verify the result.

𝟱. 𝗔𝗜 𝗰𝗮𝗻 𝗰𝗿𝗲𝗮𝘁𝗲 𝘁𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗱𝗲𝗯𝘁 𝗳𝗮𝘀𝘁𝗲𝗿 𝗶𝗳 𝘁𝗵𝗲 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 𝗶𝘀 𝘄𝗲𝗮𝗸

Milko was honest that at some point, it was visible AI had written a lot of the code.

That led them to improve the process with code quality checks, unit tests, linting rules and verification steps.

𝟲. 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗺𝗮𝘁𝘁𝗲𝗿𝘀

The question is not just how much information you give the agent.

The better question is:

What should the agent see, search, read and use?

One useful pattern: let the agent write helper scripts that make its own work easier.

𝟳. 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝘀𝗵𝗼𝘂𝗹𝗱 𝗯𝗲 𝗽𝗮𝗿𝘁 𝗼𝗳 𝘁𝗵𝗲 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄

Their workflow includes steps for analyzing documentation impact and updating internal or public documentation when code changes.

𝗙𝗶𝗻𝗮𝗹 𝘁𝗵𝗼𝘂𝗴𝗵𝘁:

AI-native development does not remove the need for engineering discipline.

It makes engineering discipline even more important.

If your process is weak, AI will help you create problems faster.

If your process is strong, AI can become a serious force multiplier for teams.

𝗧𝗵𝗮𝗻𝗸𝘀 𝗮𝗴𝗮𝗶𝗻 𝘁𝗼 𝗠𝗶𝗹𝗸𝗼 𝗳𝗼𝗿 𝘀𝗵𝗮𝗿𝗶𝗻𝗴 𝘄𝗵𝗮𝘁 𝗶𝘀 𝗵𝗮𝗽𝗽𝗲𝗻𝗶𝗻𝗴 𝗶𝗻 𝘁𝗵𝗲 𝘁𝗿𝗲𝗻𝗰𝗵𝗲𝘀.

𝗝𝗼𝗶𝗻 𝘁𝗵𝗲 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆 𝘁𝗼 𝘄𝗮𝘁𝗰𝗵 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝘄𝗲𝗯𝗶𝗻𝗮𝗿 𝗿𝗲𝗰𝗼𝗿𝗱𝗶𝗻𝗴:
https://join.aiempowered.dev/

Address

B.H. 18
Sofia
1000

Website

, ,

Alerts

Be the first to know and let us send you an email when AI Empowered Devs posts news and promotions. Your email address will not be used for any other purpose, and you can unsubscribe at any time.

Contact The Business

Send a message to AI Empowered Devs:

Shortcuts

Share

Category