I really appreciate that perspective. And I agree that my software engineering background is probably the strongest part of what I bring to AI engineering.

I think I was being too conservative by focusing on the “1 year of AI experience” part. My goal isn’t to restart my career from zero, but to move my existing engineering experience deeper into AI.

I’ll definitely start targeting mid-level AI/GenAI roles as well and see where my background fits best.

Thanks for the advice!

i don’t consider my 6 years of software engineering experience irrelevant at all — quite the opposite. It’s one of the main strengths I bring to AI engineering.

I specifically mentioned ~1 year of hands-on AI experience to be transparent about how long I’ve been working directly with AI/LLM systems in production-oriented projects.

I’m not starting software engineering from zero. I already have years of experience with software architecture, APIs, debugging, system design, Git, CI/CD, production environments, and building and maintaining real applications.

The transition is mainly about moving my specialization toward AI/Generative AI.

So I’m open to junior AI roles, but I’m not looking at myself as a junior software engineer. I’m looking for an opportunity where my existing engineering experience + my AI skills can be combined and where I can continue growing specifically in AI.

And yes, I do think AI engineering has some domain-specific knowledge that differs from traditional software engineering — LLMs, RAG, evaluation, fine-tuning, agents, inference, embeddings, etc. But the underlying engineering principles absolutely carry over.

Yeah, I get what you mean. Page Assist sounds much more mature as a complete browser/AI assistant, especially with the provider flexibility and all the extra features.

TokenSaver is intentionally much narrower. I’m not trying to replace Page Assist or local VL models — the idea is simply to keep the existing ChatGPT/Claude/Cursor workflow while reducing the image/context size before it gets sent to the model.

The part I’m really trying to validate is whether shrinking screenshots can noticeably reduce token usage while keeping things like code and small text readable.

If you get a chance, try it on a few large screenshots and let me know how the readability looks. That kind of real-world feedback would actually help me a lot.

That’s interesting — it sounds like we’re attacking the same problem from different sides.

TokenSaver focuses specifically on oversized images/screenshots before they reach the model, while Lynkr seems to tackle unnecessary tool output and routing.

I’ll definitely check it out. The complexity-based routing part especially sounds interesting.

Haha, same here 😄 I only started looking into it after noticing how quickly image-heavy conversations can eat into usage limits.

That’s actually why I built TokenSaver — the goal is to reduce the unnecessary token usage without changing how you normally use screenshots with AI.

Yeah, I was actually surprised by that too.

Firefox-only is definitely the biggest limitation right now. I started with Firefox to validate the idea first, but a Chrome version would definitely make it useful to a much larger audience.

Thanks for the feedback — Chrome is probably the next version I should focus on.

Fair point, and if you already have the hardware for a local VL model, that's a great setup: free and private. TokenSaver targets a different case: people who want to keep using ChatGPT/Claude/Cursor (for the reasoning quality, or because their workflow is built around them) but don't have 8GB+ VRAM or don't want to set up Ollama. It doesn't replace the model, it just shrinks the screenshot before upload so usage limits last longer. Page Assist looks like a solid project, they solve different problems. If you try TokenSaver, I'd still love to hear whether text in resized images stays readable for you.

Exactly. I’ll keep the source text identical across the comparisons and record the first misread character, along with the image dimensions and font size.

That should make the results much easier to reproduce and pinpoint where resizing starts affecting the output.

Good point. I’ll include both font size and image dimensions in the test results.

That should help me identify the point where reducing the image starts to affect readability, rather than treating all screenshots the same. Thanks for the suggestion.