One of my New Year's resolutions was to journal more. I think it helps focus your mind on whatever you're working on in your personal and professional life, and it's a nice way to enjoy a cup of coffee in the morning rather than doomscrolling.
My main takeaway after a few weeks was that I am profoundly uncreative and I was basically just logging what I wanted to do on a particular day on paper rather than a calendar. So it was like a less-helpful, analog version of Notion.
Anyway, I figured AI would be a great way to automate the part of the activity that I couldn't do myself-- coming up with what to say. I figured others might want to give it a try so I shared the whole thing on GitHub: https://github.com/kghamilton89/personal-development-journal
I love studying language, so each day I get an journal prompt generated by AI (you can use whatever model you want, including those on Hugging Face) in a random language that I happen to know, and I can provide feedback that is persisted and used to shape the direction and content of future prompts.
Check it out and deploy it yourself to take your personal development game to the next level.
π Like everyone else, I've been blown away by the possibilities unlocked by OpenClaw (I've got an agent running locally and in a Railway pod that's always alive so I can automate as I ride the metro).
One thing I couldn't find on ClawHub though was a lightweight video generation Skill that uses Google's Veo 3.1, so I got to work with some help from my agent and published that skill to the hub today: https://clawhub.ai/kghamilton89/veo-video-generator
π Now your agent can generate SOTA audio/video as you fervently message it from Telegram Messenger demanding minor adjustments. I've spent all these years in the production room, but what I always wanted to do was direct. Feels good man.
π One of the coolest parts about being an early Strawberry user has been the opportunity to build on the app at the ground floor.
The platform already has a ton of great integrations that let you interact with your external apps directly with tools, but I wanted to add the ability to do stuff in Slack as well.
πͺ So I took the base Anthropic Slack MCP server, added a whole bunch of new tools, and generalized it as an HTTP-based SSE-server and deployed it in like 2 minutes with Railway so that Strawberry could make use of it (as can Claude or any other MCP client).
Now, you can Chat with your Strawberry Companion (or Claude, or whatever) and do things like: β‘οΈ Get caught up across all of your Slack channels after a long weekend or noisy incident without having to read 20 threads in 10 different channels β‘οΈ Create, read, and edit Canvases, Messages, and Channels β‘οΈ Take any resources or content that you're using in your Chat and inject it directly into Slack without copy / paste
π I'm pretty pleased with the results, and I made a short demo video showing the results of the work (link in comments). The best part is, it's available on GitHub for anyone else to use too (link in the comments, instructions in the README). The setup takes about 5-10 minutes.
What a trip. Just walked through @burtenshaw and @evalstate tutorial on adding Hugging Face Skills to your Claude Code agent so you can fine tune LLMs by chatting with AI.
These are the kinds of innovations that are going to help everyone benefit from the power of Artificial Intelligence. Well done gentlemen and thank you for sharing.
π I keep seeing takes on LinkedIn from American business influencers melting down about Silicon Valley startup "dependence" on open-source Chinese models.
π€ Can anyone describe a credible scenario where these models can be leveraged by the Chinese government to endanger American security interests or am I right to believe that this is just Red Scare nonsense?
A few months ago, I built a quick POC in Hugging Face that used a fine-tuned variant of OpenAI's OSS-20B model that I trained to convert the text from pre-reform Russian-language documents into modern Russian orthography.
β‘οΈ This morning, I launched novoyaz.io.
This is a production app, the frontend for which I built in like two hours with Lovable, that uses that same fine-tuned model for transliteration, but now has a bunch of extra features that make using it even easier (like taking and uploading pictures with your on-device camera for example π ).
π If you're a researcher, or know a researcher, for whom this app will improve their day-to-day workflows, please get in touch with me.
Why I think local, open-source models will eventually win.
The most useful AI applications are moving toward multi-turn agentic behavior: systems that take hundreds or even thousands of iterative steps to complete a task, e.g. Claude Code, computer-control agents that click, type, and test repeatedly.
In these cases, the power of the model is not how smart it is per token, but in how quickly it can interact with its environment and tools across many steps. In that regime, model quality becomes secondary to latency.
An open-source model that can call tools quickly, check that the right thing was clicked, or verify that a code change actually passes tests can easily outperform a slightly βsmarterβ closed model that has to make remote API calls for every move.
Eventually, the balance tips: it becomes impractical for an agent to rely on remote inference for every micro-action. Just as no one would tolerate a keyboard that required a network request per keystroke, users wonβt accept agent workflows bottlenecked by latency. All devices will ship with local, open-source models that are βgood enoughβ and the expectation will shift toward everything running locally. Itβll happen sooner than most people think.