denn-gubsky

I'm using cloud models for development, and many local models for internal works. Local models a good for doing the part of work related to personal user information, internal functions, like facts extraction and chats consolidation, web search and market research, internal documents preparation and handling. I also use local decision models (nimble, clef) for classification and other related staff, local embedding and voice recognition models.

I prefer self-hosted and hardware password managers. This does not completely mitigate the risk, but makes the attack surface smaller. Another question is your local network perimeter protection. I don't feel safe even with reverse proxy like tailscale, and thinking about local LLM Agent based network safety monitor.

I've built a robust self-hosted OSS agentic runtime. Look for loomcycle on github.

I think that LLM Agents will communicate with each other using internal embeddings - the natural language LLM thinking on. The final output may be a platform-specific assembly or binary output. When specifically asked, the LLM Agent may present the architecture and structure in human-redable language or on diagrams. I do not see the real reason for LLMs to support so many programming languages created by humans and for humans.

I created a couple of agentic workflows to scan tech news, filter and create short digests. 5 top news is enough per day. 5 minutes reading. The rest of the time I can concentrate on things which matter for me the most. The next step is to automate all the boring stuff like accounting, taxes, funds management etc. Now I have time for family, sport and new agentic software development. The last one is just for fun, because nobody uses it but me :)

I had not access to my pull requests and CI for few hours. Nice unicorn was presented instead :)

I used Arduino sets to teach kids electronics and minimal programming. We created various simple devices with sensors and motors using resistors, capacitors and transistors to construct interfaces between digital inputs/outputs and analog devices. This way kids understand why we need analog components in digital circuits. The simplest possible example is R + LED to limit the current. You can explain that R is a thin tube which limits amount of electrons flowing to the LED. Then you can demo the R-divider to cut the voltage on the digital input. Then add a capacitor and demo kids the slowly flashing LED driven by digital output. Use oscilloscope to visualize the signal - it's hard to understand how capacitor works. Then use transistors and MOSFETs to drive motors. I also created a 3D printed parts to assemble working models like the radar, barrier and real clock driven by the stepper motor.

Ryzen 7 8700G is an APU, and it has built-in Radeon 780M GPU (12 CUs, ~12.6 TFLOPS, plus an NPU). This is not very strong, but this is floor which runs local models for me and saves a lot of paid tokens. Potentially this setup may be upgraded to AI MAX when AM5 APUs will become available in box packages (now they are OEM-availabe only). The most expensive part of my system is DDR5, and it may be reused in case of upgrade. Actually, this is my self-hosted NAS (TrueNAS Scale) which also runs Ollama and serves as my local inference machine.

I'm running qwen3.6, gpt-oss and embeddinggemma with Ollama on Ryzen 7 8700G + 96 GB DDR5 with 12-14 tokens/sec. Consider this as a floor. On Strix Halo it will run 4-6 times faster depending on memory bandwidth. For my local task 14 tok/s is quite enough for the price I paid.

I'm building the agentic runtime and agents running on this platform. The keystone of agentic work is "allowlists vs denylists" choice. On my platform I use allowlists for everything (tools, MCPs, skills, memories etc.). Agents work in a sandbox and have only allowed resources they need for functioning. Nothing more. The LLM is a probabilistic by nature, it can try to access or delete the resource without a reason, it may follow the prompt injection, it may leak the secret or private data. So all this should be controlled by the agentic runtime. In my system I use specialized agents as team players - each agent does it's part of work using available resources. This way you can keep all needed data in the model's context window without compaction and optimize resources control. Ask more specific question if you need a detailed answer.

I develop 5-10 times more complex projects now without anticipation to never complete them. And I can do it alone with team of agents running 24/7. Maybe these new projects will not be used by anyone, but I feel more confident in previously unknown for me domains like robotics, agentic runtimes or GraphRAG. So my advice: just keep evolving.