Accenture sells AI transformation to thousands of clients while quietly telling its own employees they get a fraction of what some were already spending. The internal memo reveals a tension at the heart of every major consulting firm racing to profit from AI.
Why even bother at all? What can you do with those tokens? Ask for the weather twice?
The Haiku 5.5 model is actually quite inexpensive. I’ve used it to implement multiple features this month and have only spent $4 of my $20 monthly budget.
Are you using api or subscription?
Technically neither - I use it via Microsoft Co-pilot subscription, and it’s a la carte for model usage from every provider.
Ah co pilot was much worse than claude code when I used it. Subscription is far far better the api prices are outrageous
Microsoft’s copilot branding is all over the place. Copilot CLI and GitHub copilot (IDE plugin) both let you choose which model you want to use.
Yes but not using claude code as a harness results in worse use out of their models from my testing
Let’s not forget Copilot is also an office suite lol
Everything is copilot. No more windows, copilot is my operating system. Microsoft has changed its name to Copilot, Inc., and Satya Nadella has updated his CopilotedIn profile name to Copilot Copilot. Copilot is my copilot.
The legacy code I’m managing is a clusterfuck of the highest order. Even Sonnet struggles to analyze it sometimes 😑
I’m happy this is only a small part of my job, but oh my…
Same. AI has fallen down repeatedly trying to analyse both of my repos. And they were written by a bad human originally. The problems are all the same, except AI will get you spaghetti faster.
Messy brownfield code requires a lot more meta-analysis from previous sessions, just to get all of the spaghetti code into a manageable set of rules and documentation.
Don’t just send the agent blindly into the code. Every session, the agent is just going in fresh without any context on how the code works. Break the codebase down to a 10,000 foot view as a summary, and then get more documentation and agent rules for each of its major sections. Good tests help a great deal, too, and if there aren’t tests, start building them. Start creating skills just for the codebase, or any common tasks you’re asked to do against it.
Also remember that you don’t have to write any of this stuff. Have it write the docs, rules, skills, tests, and just review it to make sure it looks right.
That is a pitfall that is difficult to mitigate. When using these models your own code base is the primary input, and it will match the style. Garbage in, garbage out. It may be worthwhile to refactor prior using a model. Even having intelligible method and variable names can make a difference. Maybe do a sweep of simple renaming, and adding supplemental explanatory comments, then you might get better results?