Have you heard about GitHub Copilot? Probably yes. Some of us (I) even pay for it. But are there alternatives and how the AI coding assistant market looks like? Recently I've spent some time to study this and want to share my findings.

🎥 AI assistance beyond code: what do we need to make it work?

Nice talk from one of the persons I was working with. She shared ThoughtWorks experience in investigating the AI assistance field. Key takeaways:
1️⃣ For AI it's right now easier to CREATE new code, rather than CHANGE the existing one. This is why we see lots of demos of how unexperienced people create new stuff using AI (cursor + v0). But we do not see how it is used at scale or the tasks we do daily — there is a large code base, you need to integrate feature.
2️⃣ AI is good in helping you to think in the divergent way. For example — put the story draft there and ask what have you missed? I till help you to improve it and check it from different angles.
3️⃣ How can it help on the organisation level? Codify practices and sharing prompts. Do same things the same way in the organisation. For example, create a prompt for STRIDE analysis and do it the same way always.
4️⃣ Tools like Continue allow you to create custom slash commands (/stride), this way you can share it within the organisation.
5️⃣ AI works better when it has enough of context. It is not enough to just use an LMM which was trained on big amount of data. You need to add YOUR context (code base, wiki, domain expert input, stuff which is unique to your organisation). There is no answer for this question yet, but tools try to start solving it, for example Continue allows you to add context together with your prompt.
6️⃣ Current market state — no magic without some investment. There is no one tool, we need to think how to integrate it in the best way. Right now, choosing to use AI on the organisation (not individual) level always add a big overhead.

I produced too much content for one post, so I will post the 2nd part tomorrow. There is more to share 😉