Microsoft Amplifier
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AI Coding AssistantsMicrosoftSoftware Development
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AI Coding Assistants
Microsoft
Software Development
Microsoft released Amplifier, an AI-powered coding assistant framework on GitHub, sparking debate among developers about its value, potential, and the role of AI in coding.
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Some people in the organization will experience the limitations and some will learn — although there are bound to be people elsewhere in the organization who have a vested interest in not learning anything and pushing the product regardless.
claude Claude
Interesting given Microsoft’s history with OpenAI
https://techcrunch.com/2025/09/09/microsoft-to-lessen-relian...
This stood out to me too, seems like a months-long project with heavy use of Claude
The Austrian army already switched to LibreOffice for security reasons, we don't need another spyware and code stealing tool.
There are many many people who want better AI coding tools, myself included. It might or might not fail, but there is a clear and strong opportunity here, that it would be foolish of any large tech company to not pursue.
https://news.ycombinator.com/item?id=45540174
I would say it’s more the result of anti competitive bundling of cloud things into existing enterprise contracts rather than the wave. Microsoft is far worse than it ever was in the 90s but there’s no semblance of antitrust action in America.
If an “objective” test purports to show that AI is more creative than humans then I’m sorry but the test is deeply flawed. I don’t even need to look at the methodology to confidently state that.
https://en.wikipedia.org/wiki/Torrance_Tests_of_Creative_Thi...
You think it is creative because you lack the knowledge of what it has learnt.
This project was in part written by Claude, so for better or worse I think we're at least 3 levels deep here (AI-written code which directs an AI to direct other AIs to write code).
Most models I've benchmarked, even the expensive proprietary models, tend to lose coherence when the context grows beyond a certain size. The thing is, they typically do not need the entire context to perform whatever step of the process is currently going on.
And there appears to be a lot of experimentation going on along the line of having subagents in charge of curating the long term view of the context to feed more focused work items to other subagents, and I find that genuinely intriguing.
My hope is that this approach will eventually become refined enough that we'll get dependable capability out of cheap open weight models. That might come in darn handy, depending on the blast radius of the bubble burst.
If this is restoring the entire context (and looking at the source code, it seems like it is just reloading the entire context) how does this not result in an infinite compaction loop?
Also, it can be useful to compact before it is strictly necessary to compact (before you are at max context length). So there could be a case where you decide you need to "undo" one of these types of early compactions for some reason.
Generally when your investment fails you don’t get paid back, right?
I tried it with a feature, took about 10 minutes and a lot of iterations, and would easily have used hundreds of thousands of tokens. Doing this 20, 30 times a day would be crazy expensive.
People are correct to question it.
If anything, Microsoft needs to show something meaningful to make people believe it's worth trying it out.
I see a possible paradox here.
For exploration, my goal is _to learn_. Trying out multiple things is not wasting time, it's an intensive learning experience. It's not about finding what works fast, but understanding why the thing that works best works best. I want to go through it. Maybe that's just me though, and most people just want to get it done quickly.
WARNING: Claude Code running in Bypass Permissions mode │ │ │ │ In Bypass Permissions mode, Claude Code will not ask for your approval before running potentially dangerous commands. │ │ This mode should only be used in a sandboxed container/VM that has restricted internet access and can easily be restored if damaged.
Caution
This project is a research demonstrator. It is in early development and may change significantly. Using permissive AI tools in your repository requires careful attention to security considerations and careful human supervision, and even then things can still go wrong. Use it with caution, and at your own risk.
and
requires careful attention to security considerations and careful human supervision
is a bit orthogonal no?
“Using permissive AI tools [that is, ones that do not ask for your approval] in your repository requires careful attention to security considerations and careful human supervision”. Supervision isn’t necessarily approving every action: it might be as simple as inspecting the work after it’s done. And security considerations might mean to perform the work in a sandbox where it can’t impact anything of value.
This is gaining stars and forks but I don't know if that's just because it's under the github.com/microsoft, and I don't really know how much that means.
I'd rather have the three word message than detailed but wrong messages.
I think I agree with you anyway on average. Most of the time a claude-authored commit message is better than a garbage message.
But it's still a red flag that the project may be filled with holes and not really ready for other people. It's just so easy to vibe your way to a project that works for you but is buggy and missing tons of features for anyone who strays from your use case.
I'd never encourage anyone to blind commit the messages But if they are correct they seem a lot more useful than 90% of commit messages.
I found the biggest mistakes that I've seen other people do are like - they move a file, and the commit message acts like it's a brand new feature they added because the llm doesn't put it together it's just a moved file
At what cost,. monetary and environmental?
As costs drop exponentially (a reasonable expectation for LLMs, etc.) then increasing agent parallelism becomes more and more economically viable over time.
Not a reasonable expectation anymore. Moore's Law has been dead for more than a decade and we're getting close to physical limits.
This is kind of how I feel. Chat as an interaction is mentally taxing for me.
So if you can get the spec right, and the LLM+agent harness is good enough, you can move much, much faster. It's not always true to the same degree, obviously.
Getting the spec right, and knowing what tasks to use it on -- that's the hard part that people are grappling with, in most contexts.
But in all seriousness +1 can recommend this method.
Codex is like an external consultant. You give it specs and it quietly putters away and only stops when the feature is done.
Claude is built more like a pair programmer, it displays changes live, "talks" about what it's doing and what's working et.
It's really, REALLY hard to abort codex mid-run to correct it. With Claude it's a lot easier when you see it doing something stupid or getting of the rails. Just hit ESC and tell it where it went wrong (like use task build, don't build it manually or use markdownlint, don't spend 5 minutes editing the markdown line by line).
But I thought there are lots of agentic systems that loop back and ask for approval every few steps, or after every agent does its piece. Is that not the case?
That's cute
The secret weapon to this approach is asking for 2-4 solutions to your prompt running in parallel. This helps avoid the most time consuming aspect of ai-coding: reviewing a large commit, and ultimately finding the approach to the ai took is hopeless or requires major revision.
By generating multiple solutions, you can cutdown investing fully into the first solution and use clever ways to select from all the 2-4 candidate solutions and usually apply a small tweak at the end. Anyone else doing something like this?
[0]: https://github.com/sutt/agro
https://xbow.com/blog/alloy-agents
I've been doing something similiar: aider+gpt-5, claude-code+sonnet, gemini-cli+2.5-pro. I want to coder-cli next.
A main problem with this approach is summarizing the different approaches before drilling down into reviewing the best approach.
Looking at a `git diff --stat` across all the model outputs can give you a good measure of if there was an existing common pattern for your requested implementation. If only one of the models adds code to a module that the others do not, it's usually a good jumping off point to exploring the differing assumptions each of the agents built towards.
I’m super not interested in hearing what people have to say from a distance without actually using it.
The agent demonstrated strong architectural and organizational capabilities but suffered from critical implementation gaps across all three analyzed tasks. The primary pattern observed is a "scaffold without substance" failure mode, where the agent produces well-structured, well-documented code frameworks that either don't work at all or produce placeholder outputs instead of real functionality. Of the three tasks analyzed, two failed due to placeholder/mock implementations (Cross-Repo Improvement Tool, Email Drafting Tool), and one failed due to insufficient verification of factual claims (GDPVAL Extraction). The common thread is a lack of validation and testing before delivery, combined with a tendency to prioritize architecture over functional implementation.
For context, we are right in the middle of building this thing... multiple rebuilds daily since we are using it to build itself. The value isn't in the code itself, yet, but in the approaches (UNIX philosophy, meta-cognitive recipes, etc.)
We are really excited about how productive these approaches are even in this early stage. We are able to have amplifier go off make significant progress unattended for sometimes hours at a time. This, of course, raises a lot of questions on how software will be built in the near future... questions which we are leaning into.
Most of our team's projects, unless they have some unresolved IP or are using internal-only systems, are built in the open. This is a research project at this stage. We recognize this approach it too expensive and too hacky for most independent developers (we're spending thousands of dollars daily on tokens). But once the patterns are identified, we expect we'll all find ways to make them more accessible.
The whole point of this is to experiment and learn fast.
Again this "supercharging" nonsense? Maybe in Satiyas confabulated AI-powered universe, but not in the real world I am afraid...
https://paradox921.medium.com/amplifier-notes-from-an-experi...
For those who find it useful in this very early stage, to find some value for yourself in either using it or learning from it, happy to be on the journey together. For those who don't like it or don't understand why or what we're doing, I apologize again, it's definitely not for everyone at this stage, if ever, so no offense taken.
I have a repo that shows you how to do this stuff the correct way that's very easy to adapt, along with a detailed explanation, just do yourself a favor, skip the amateur hour re-implementations and instrument/silo your agents properly: https://sibylline.dev/articles/2025-10-04-hacking-claude-cod...
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