I have been using Emacs for more than a decade, and I was always excited about the features. But with AI, something has changed. I no longer type/edit that much. Recently, LSP stopped working, and I was completely oblivious to it for about a week. Earlier, something like this would have been a major annoyance.
i am still working on easyanalytica tool to auto generate dashboards without ai .
I recently added comparison feature and figuring that out was fun. There are lot of interesting ideas on execution side of it but for end user its a simple product, just give data and see the dashboard.
html snippet playground - for testing html/react snippets
token speed calculator - for estimating tg/s of ai based on ram speed and model size/params this helps in comparing different hw, estimating likely speeds i will get on hardware
prompt assembler - to create prompt and context once and reuse it in different ai's, picking and choosing context in a prompt, creating agent.md etc.
dashboard builder - for viewing gsc, ga, stripe data in one place
yes i keep on trying small models, i have also tried qwen 3.5 0.8B, 2B, 4b and gemma4 e4B models but they either did not worked reliably (thinking loop, issue in following instruction) or there were performance issues (prompt speed, tg speed, too much ram) e2b was the sweet spot where i could give it plan and it can edit files properly.
i use smaller model gemma e2b for most of my editing and it works surprisingly well. Workflow is planning with sota models and execution via small models. If you plan properly dont leave ambiguity for smaller model it works well.
why do people want to continue to use anthropic despite their shitty service? its not like they have some kind of lock-in as it is still new company and it has shown its color before we are stuck with it unlike google/meta etc.
I did a showhn with similar idea(got a whooping 1 point and was flagged as spam which was later removed by mods), you paste your html and it encodes it into url, you can share the url without server involvement. I even added a url shortener because while technically feasible encoded url becomes long and QR code no longer works reliably. I also added annotation so you can add your comments and pass it to colleagues.
that guy is not including ffmpeg and is not encoding in browser. What he is doing is generating a ffmpeg command that you can run on your cli/scripts etc.
"We ran our own analysis sampling 150 profiles per repo across 20 projects and found repos where 36-76% of stargazers have zero followers and fork-to-star ratios 10x below organic baselines"
This does not looks like appropriate signal to use on github, i doubt that this is organic baseline.If this is used as metric than study might be flawed.
correct but it should be some ratio of model size like if model size is x GB, max context would occupy x * some constant of RAM. For quantized version assuming its 18GB for Q4 it should be able to support 64-128k with this mac
what was your data size? i am surprised 800kb made a difference? using stringzilla was smart approach,my guess is it being unusually faster made all the difference.
i doubt it would be possible, it boils down to compression problem compressing x amount of content to y bits, since content is unpredictable it cannot be done without having intermediary to store it.