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i am not clear what exactly will these agents do? are they like ai sales rep for brand for talking to user if they want to explore the product further?

working on adding webmcp support to my new free tool https://easyanalytica.com/tools/gsc-insight , webmcp was recently added to codex desktop app and it would be interesting to see how users use it with ai, would it get good adoption or it would just go away.

i made my own html playground that is browser only and shares preview via url hash because of these useless shenanigans of codepen, its not as featureful but i mostly use it for sharing single page html files

https://easyanalytica.com/tools/html-playground/

what are you using for reading/writing pdf? is it some mcp plugin or some pdf lib in python/js and qwen is doing it via code?

GSC Insight is a free browser-based tool that turns Google Search Console CSV exports into a visual SEO dashboard.

It helps you quickly spot:

- Quick-win keywords

- Content gaps

- Page and query opportunities

- Branded vs. non-branded traffic

- Ranking and click distribution

- Country-level performance

You can also add your logo and add title and create reports

- Free

- No Registration/login

- No installations(online)

- No GSC connection or server upload

- works offline after first load - your CSV data is processed locally in the browser.

i am using llama.cpp with pi harness on linux(cachyos). some of the things like coopmat are disabled in linux due to intel driver issues otherwise performance would be even better.

i have same 255h and i was able to run it with low token speed 6-8tg/s with approx similar context window 60k

+1. i have similar workflow and i use local models on igpu so token cost is free and electricity cost is in cents.

yes, happened to me as well on sideprojects and it continues, i am able to post on other groups, its weird.

I am building https://EasyAnalytica.com - single place for all your dashboards. It generates dashboards automatically from data without using ai. It supports getting data from google sheets, api's, url's etc. I have recently added support for gsc as data source and i plan to continue adding more in coming weeks.

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.

what bug and it affects what?

yes

harness - pi+custom extension for subagents

model - qwen3.6 35ba3b q4km

hardware - intel arrow lake with 32gb ram

server - llama.cpp vulkan

performance - 15-18t/s generation 50-150t/s pp

planning and task creation is still using claude/gpt but they dont touch the code. All coding is done using this setup.

Example of project made using this setup easyanalytica.com , its of medium size complexity

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

afaik, enterprise plans are not subsidized. its 20$/seat+api pricing. Unless you are saying api pricing itself is subsidized.

depends on how clear your instructions are, if there is no ambiguity you can even use gemma4 e2b/e4b.

i did not see much improvement for my use case i.e. file editing tasks but with e4b tg/s is lower so i stick with e2b.

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.

- Tool for organizing files, pasted data, and prompts into markdown snippets you can copy into different AI chats.

- Calculator that gives tg/s and vram required based on model params and ddr settings.

- Auto create dashboard from csv/json files or apis Easyanalytica.com

- snippet viewer for html/react that allows annotation and sharing based on url fragments

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.

https://easyanalytica.com/tools/html-playground/

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.

PS: i am that guy :-)

i was with them until

"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