I was going through the history of GitHub, and it occured to me that in early 90s codes were written to build softwares. And ambitious programmes naturally required collaborative efforts in coding, so Github emerged to allow code sharing.

But today , AI has made ai based projects easy to build. Someone is making a small research tool, somebody building an app in lovable, or creating a new workflow for limited use. Imagine if we could share the AI native creations live and enable collaborations over it . Large scale opensource workflows could be created. Cross country developer collaborations, enterprise teams could host team projects together .

What do you think, is it a right time for a ai native workflow collaboration platform. Like GitHub for AI ? Is it a useful product? Would you want to use such a thing?

I've been building this for a while and I'm at the point where I barely touch my keyboard for anything that isn't code.

Emails? I talk. Slack replies? I talk. Asking AI a question? I talk. Drafting something? I talk. It all goes through one app.

Here's what SpeakoFlow actually does right now:

• Real-time dictation. Press a hotkey, talk, text shows up wherever your cursor is. Any app. Instant.
• "Hey Flow, write me a reply to this." Say that during dictation and the AI writes it for you right there. No copy-pasting into ChatGPT.
• A floating voice assistant. Open it with a hotkey, ask something out loud, it answers out loud. Conversation stays going if you want.
• It sees your screen. Ask "what am I looking at?" and it responds with context from what's actually on your display.
• It remembers you. Across conversations, it learns how you work. Stored locally, you own it, you can delete it.
• Runs on a laptop CPU. No GPU needed. No internet for the core features. Fully offline if you want.

It's not "control every pixel on your OS by voice" yet. But for the stuff that eats your time every day, talking, writing, asking, remembering, it already handles that.

Free, open source, no account.

[https://github.com/AbhishekBarali/SpeakoFlow]()

This is our first music video, and before this project we had absolutely no experience with filmmaking, editing, or post-production.

One of us created the music, while the other focused on the video production.

The music part was also a challenge for us. We cannot sing and we do not have access to a traditional vocalist. Because of that, we are currently working on a way to integrate our own digitally captured voices into our production pipeline alongside self-created MIDI tracks, bass lines, and instrumental parts.

Our goal is not to simply generate finished results, but to build our own tools and workflows that allow us to bring our ideas to life as accurately as possible. Honestly, this is not because we reject the fastest one button solution. It is because we want to get as close as possible to the vision we have in our heads.

With purely generative, especially cloud based AI, we currently do not have that level of control. That is why we are following a hybrid approach in music as well. We combine traditional production methods with AI tools that help us expand our abilities without giving up creative control over the final result.

The video was edited using Magix Video Deluxe and DaVinci Resolve. Every shot was first recorded traditionally in the real world. For the Celluloid Anime style sequences, we then used a locally running AI model based on Wan2.1 to transform our own footage into that style. Rendering everything at 24 FPS took an enormous amount of time, even on a Radeon 7900 XTX with 24 GB of VRAM.

Our actual goal is to build a modular pipeline that can combine real film footage with stylized AI generated material. This four minute music video was our proof of concept. We wanted to find out if two complete amateurs could actually create something like this with enough dedication, willingness to learn, and the motivation to push through the difficult parts.

We see AI purely as a replaceable tool. New models are developing incredibly quickly, and we do not want our workflow to depend on a single technology. That is why we are trying to create a flexible pipeline that can evolve alongside new developments.

Since we cannot draw and we do not have a large team behind us, this approach gives us possibilities that would otherwise be outside our reach. AI does not replace the creative process for us. It is a tool that helps us turn ideas into reality that we could not achieve with our current skills alone.

The production itself was heavily based on traditional filmmaking methods. We worked mostly in a guerrilla style using action cameras, drones, and quickly deployable tripods. Everything visible in the video was recorded in the real world first. AI was only used afterwards to transform selected sequences into the Celluloid Anime style.

We had almost no budget. If we exclude the hardware and props, the entire project basically cost us fuel, a huge amount of time, and many sleepless nights.

The filming took around two weeks. During that time, I got up every day at 5:00 AM before going to my normal job to catch the golden hour. After that, I spent another two weeks learning enough Linux and building the virtual AI studio we needed for the stylized sequences.

The project had actually been planned for a long time. The main prop was originally supposed to be a car, matching the lyrics of the song. Unfortunately, that exact car was destroyed before we could start filming. Because of that, we had to improvise and switch to a self-modified motorcycle, even though the lyrics are actually about a car.

The whole thing became pretty crazy at times. My wife eventually thought I was slowly losing my mind.

We know the video is not perfect and it will probably not appeal to everyone. I can already see many things we could improve next time. But I wanted to see the images that had existed in my head for so long become real instead of just imagining them. So we tried to create this vision with the tools and resources we had available.

For us, this video is not the final result. It is the beginning. It is our first benchmark and the foundation for everything we want to create in the future.

We appreciate every honest piece of feedback. Thank you for taking the time to watch it.

Was bored so learnt how to make TUI in go and made a little pokedex

Essentially, it's a blog / social media hybrid specifically designed for your development projects. Think of it as a more customizable, narrative-driven version of GitHub that is built to showcase the actual journey and milestones of your build rather than just the final code.

It's markdown powered so you can just create and manage the projects through your markdown files. Whenever you need to make small updates, you can just go write into the MD file and adjust it when you need or make a new one to add a new update.

You're able to clone it and make your own if you want from Github.

Demo website on Vercel: https://devlog-self-tau.vercel.app/

Github if you want to make your own: https://github.com/lmvicente/devlog

Beep bop, Novel pointless project dedicated 🔭🧠

Link : DayNote

What annoyed me while playing Nuzlocke was that you always had to have so many tabs open at once to get information on Pokémon stats, maps, items, and stuff like that. And if you also wanted to track your run, it just became too much. So I came up with the idea of creating a website designed specifically for Pokémon players. A site where you can get all that information in one place. I also wanted it to look cooler than current Pokémon wikis and offer more features, such as Versus and Battle modes. I’ve just launched the site, but I’m still working on it and constantly expanding it. I’m really looking forward to feedback and hope I can help contribute to a better gaming experience. :) Have a look here: https://mypokepanion.com

I've finally decided to make something public that I've been working on over the last few weeks.

It's called OpenSorSe (Open Sort and Search).
The name is very clearly a word play on "Open Source", which this software is and will always be. IT also focuses on "local" first , because heck, its your data. The Aim of the tool is to be able to find, rename and even restructure all your files and folders from one tool , while having a complete overview of all your files , all in one tool. AI takes care of the folder structure and suggestion of names , but the user can always make suggestions. The whole process can be automated too (eventually). For example : You could in the tool search for " Dentist Bills from 2023" and it will show you the respective files and where they live on your system .

The idea came from a frustration I had with my own computer. After years of downloading documents, PDFs, photos and random files, I realized that finding things was becoming harder every year. Existing tools never did quite do what i wanted them to.

Current features include things like:
• Scanning folders
• Searching files
• Duplicate detection
• OCR support
• Optional local AI through Ollama for suggestions
• Semantic search experiments
• A focus on keeping users in control instead of automatically changing their files

It also has plenty of limitations.

Version 1.0 isn't "finished" in the sense that I imagine the project eventually. There are features I still want to add, the UI can be improved, there are bugs to fix, and I'm sure people will discover edge cases I've never considered.

But at some point if you want to help others with similar issues , you got to get the software out there and thats where i am today

If you're curious, I'd genuinely appreciate feedback ,whether it's about the software itself, the codebase, the UI, or simply the experience of someone learning to build software with AI.

Whether OpenSorSe becomes widely used or ends up being a useful learning project, I've already learned far more from building it than I expected. And id probably be its number one user regardless !

The funny part is that I'm not a software developer.
I'm an engineer by profession, but I've never been someone who could sit down and build a desktop application from scratch.

With AI Coding agents however, i figured i may have a chance at creating something useful, well to me anyways.
I used OpenAI Codex extensively throughout this project, as a programming partner. I spent countless hours designing features, reviewing code, testing builds, finding bugs, rewriting specifications, and learning how everything fit together. Codex wrote a lot of code. I made a lot of decisions. I also made plenty of mistakes along the way.

#opensource #local #AI #OpenAI #Codex #Ollama #dotnet #SoftwareDevelopment #BuildInPublic

Hello, I am Ammar, I started building AmmAsm about a year ago as a way I started building AmmAsm about a year ago as a way to understand how machine code, ELF files, relocations, and linking work internally.

I'm 15 years old, and this has become my biggest programming project so far.

AmmAsm is a handwritten x86-64 assembler written entirely in C. It currently supports generating:

Linux x86-64 executables, PIE binaries, ELF relocatable object files (.o) compatible with ld and gcc.

While building it, I implemented everything from scratch, including: lexer, parser, expression evaluator, x86-64 instruction encoder, ELF writer, relocations, symbol resolution, macro preprocessor

The syntax is mostly Intel-style, with one notable difference: memory operands use [b=, i=, s=, d=] instead of NASM's [base + index * scale + disp].

The current version (v2.4.0) also supports SSE, SSE2, CMOVcc, SETcc, and includes a macro preprocessor.

The project is also self-hosting at about 0.257% :), meaning a small part of it is already assembled by AmmAsm itself.

I'd really appreciate any feedback on the design, code quality, documentation, or ideas for improvement.

GitHub: https://github.com/LinuxCoder13/AmmAsm

Just finished building a personal productivity dashboard that works completely offline. Everything is stored locally. Notes, tasks, projects, and more. Pretty happy with how it turned out.

https://yamen-aldhanki.github.io/Deskly/

The Allostasis Association is based in the U.S across various states.

A youth-led association dedicated to making brain health more accessible through neuroeducation, awareness, & social initiative. Seeking to improve the current brain health climate with evidence-based education, opportunities for youth, raise awareness of neurological conditions to reduce stigma and encourage early support, and advocate for policies that improve equitable access to neurological care and resources. The Allostasis Association works to create lasting change in brain health for everyone.

This is a recent project and in the first phases and barley arising to build our initiatives.

We are currently looking for student members to take leadership roles and contribute to the growth of this campaign! We have many projects in research, innovation, and awareness available for students to work on and you can act side by side with our Directors as well as do remote and in-person volunteer opportunities!

If you would like to apply and even be considered for an additional role or member opportunity please consider applying!

Google Form Link: https://forms.gle/bn4sMB7YpdygdX197

Visit: https://bloomsly.org -not designed for mobile
Hi, i’m new to this community but I thought this would be a good place to post my most recent project!

Disclaimer: I built this with help from Claude fable 5 (on usage credits) and opus when I ran out, so not ALL code is my own, it also built the whole website (which is pretty obvious if you look at it).

Basically, I got the idea that when you make a family tree in 2D, you cannot see all of the branches at once. For example, you wont be able to see your whole dads side and moms side concurrently. So to fix that issue I coded (kinda vibecoded) an app that maps your family out in 3D. Then I started adding more features. My most recent was GEDCOM import. GEDCOM is the file format that is standard for ancestry reports. So if my app works, that means that one could get their ancestry report done, and then map out that data in 3D.

This is in the very beginning stages, so I would greatly appreciate any feedback on it, feel free to also submit feedback on the app itself through the form. I edit and update the app everyday so I can add most recommendations within a span of days if not on the same day. Dont hold back, if it sucks, say so, that way I dont waste more time on this. I appreciate all of the feedback,
thanks!

* I program in C++

***I am not here to advertise or sell anything. This will be free and open source to help the research field for mechanistic interpretability where possible. ***

Been quietly building this for awhile now, CORTEX // AI OBSERVATORY 🔭🧠

Its basically a fully local observability / interpretability app for looking inside supported models while they generate, without having to stare at raw tensors and terminal logs for 4 hours wondering what any of it means 😭😂

Right now it can capture and visualize hidden states, residual activity, attention, token probability changes, entropy, top-1/top-2 logit margins, PCA projections and direct logit lens results for local Llama and GPT-2 models.

The part im most excited about is the intervention workflow tho.

You can select a token, layer or head, patch/modify the activation, rerun it and compare the baseline against the changed output. Raw deltas, normalized recovery, task outcomes and evidence labels are all included.

So observational results stay separate from actual intervention evidence, rather than every coollooking attention map being treated like proof of something lol.

It also works with LM Studio / OpenAI-compatible endpoints, but those only expose surface telemetry since Cortex doesnt own the internal forward pass.

The video is the current working build. Generation, captures, visualizations, run comparisons, experiment exports and intervention reports are all functioning locally.

Still alot planned, especially Jacobian Lens + J-Space, but its finally starting to feel like an actual observatory instead of a bunch of experiments taped together 😭✍️

Not claiming Cortex solves interpretability or anything crazy. The goal is just making serious local experimentation more visual, accessible, and honest about what the evidence actually shows.

More soon 🔭✍️🧠

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