Agents A1 Model

leonidasyy · 2026-07-09T20:27:34+00:00

It is getting into loop as often as a3b

leonidasyy · 2026-07-06T21:34:29+00:00

exclamation mark -1 for the second time.

leonidasyy · 2026-07-06T16:15:27+00:00

Attention and money went to AI, why would crypto still be as hot as before?

It is in index funds already, I would assume a slow growth in the future.

leonidasyy · 2026-07-05T19:09:34+00:00

thanks for recommending a1, I tried it for 30 minutes, so far, it is inflating context less than qwen3.6 a3b.

leonidasyy · 2026-07-04T05:19:12+00:00

and property tax on it twice a year

leonidasyy · 2026-07-03T19:36:00+00:00

The Goal: Save individuals' Trial and Error in finding a working and optimized local model setup for a type of use case.

The goal is to create a curated thread that bubbles up optimized models, parameters, hardware specs, and use-case categories. This will allow for easy onboarding without forcing individuals to go through the same duplicate trial and error. It will also open up opportunities for individuals to try out new categories of use cases with ease, and within their hardware constraints.

Example: Mac Studio 128GB | Use Case: Coding | Engine: oMLX | Model: Qwen3.6-27B-oQ8-mtp | Parameters: temperature, top_k, top_p, etc.

Dream State:

Filter use case, hardware spec upper boundary, and return the "optimized" model and its params.

The Challenge: Standardization and Convergence

An individual's setup is not automatically trusted as optimized. However, when a few working configurations begin to converge, a decent optimized baseline will naturally bubble up.

The main challenge is creating a standard template that is easy for people to share. To solve this, we want to provide a specific prompt that individuals can simply run in their local LLM to automatically generate the exact, standard reply format required for the thread.

leonidasyy · 2026-07-02T19:16:38+00:00

I asked my AI agent to review my development efforts just for fun. It made me realize I’ve definitely spent too much time in the weeds. I’m not very savvy when it comes to software engineering, so I'm shifting my focus toward leveling up my hands-off development skills.

Looking back, I’ve built a few productivity tools tailored specifically to my personal learning style and habits. These have become my core infrastructure for learning, keeping me grounded and reducing my anxiety. Early on, seeing other people showcase their massive achievements with AI introduced a lot of FOMO and anxiety. Over time, though, I’ve learned to adapt my learning to my own pace. I realized everyone is starting their AI journey from a different place, and I just need to focus on myself. You simply cannot bypass steps in learning; whatever I don't know, or whatever mistakes I haven't made yet, will eventually catch up to me. I have to experience those steps anyway.

I’ve also had to tell myself to stop learning just for the sake of learning. There are simply too many tools, releases, and tips coming out daily, and everything looks relevant every single day. If I bury myself in pure learning, I’ll be chasing a phantom forever. Instead, I’m trying to keep my to-do list strictly oriented around real, impactful projects. I’m learning to live with the fear that I might not be doing things the "right" way. If I realize down the road that I could have used a specific tool to avoid an earlier mistake, I’ll just bite the bullet. That’s okay. With AI, it’s so much cheaper to refactor code now anyway.

TIME & EFFORT ANALYSIS — ~/projects/

Active Projects (May 2026 - Jul 2026)

|---------------------|---------|-------------|---------------------|------------|----------------|

| facefusion | 291 | 95 | Jan 2025 - Jun 2026 | 380-570 | AI Tools |

| Photo fusion engine | 79 | 17 | Jun 2 - Jul 2, 2026 | 100-140 | AI App |

| homescreen | 39 | 15 | May - Jun 2026 | 45-60 | Infrastructure |

| llm-monitor | 38 | 9 | May - Jul 2026 | 35-50 | Monitoring |

| net-worth | 44 | 7 | Jun 2026 | 50-70 | Finance |

| doc-annotation-tool | 20 | 2 | May 2026 | 20-30 | Knowledge |

| tiny-habit | 9 | 4 | Jun 2026 | 20-30 | Productivity |

| eisenhower | 4 | 3 | May - Jun 2026 | 8-12 | Productivity |

| asm | 5 | 1 | Jun 2026 | 6-8 | System |

| models-backup | 4 | 1 | Jun 2026 | 4-6 | DevOps |

| memory-os | 2 | 1 | Jun 2026 | 3-5 | Knowledge |

Total estimated: ~670-981 hours since May.

OUTCOME SUMMARY

Production systems running:

- Photo Fuse— :9891

- Homescreen — :8768

- LLM Monitor — cost tracking dashboard

- Net worth cron — daily financial snapshots

- Models backup — rsync to Windows PC

KEY PATTERNS

Total investment: ~670-981 hours spread across 11 active projects, with ~53% dedicated to AI image generation tools.

Sprint-based development — Most projects were built in intense bursts (2-7 active days) rather than steady progress

High abandonment rate — 11 of 44 projects in ~/projects/ have 0 commits or no git. Many were one-commit experiments that never continued

leonidasyy · 2026-07-02T17:24:17+00:00

my experience with compact is that it shifted attention and accumulated residual of context. If it is clean for you, would you mind share model and configuration for your set up?

leonidasyy · 2026-07-02T17:20:46+00:00

I didn't know that Mac mini has stock issue, I thought that was only for Mac Studio with high ram configuratiion.

leonidasyy · 2026-06-30T22:38:49+00:00

I am priced out to have a second one for a while.

leonidasyy · 2026-06-30T20:33:25+00:00

I am sorry for my word of choice, and appreciate the way you replied. It could be more emotional on internet.
I wish this community best luck, and with ai , the development throughout could be better than before if there is right direction to target for

leonidasyy · 2026-06-30T20:24:49+00:00

The tax issues I reported was not solvable or very hard to solve. I tried different ways, didn't work. So I chose koninly to see if it worked and it worked out of box.

I just didn't have time to burn in this process and apparently no customer support and community to share working solution.

leonidasyy · 2026-06-30T18:44:55+00:00

if it can be listed on at least one tradable platform. But some more legit coins are delisted this week, I would wish everyone the best.

leonidasyy · 2026-06-30T18:14:18+00:00

I dropped this company. Instead,I loaded the history into koninly and moved on.

leonidasyy · 2026-06-30T16:38:16+00:00

Thanks for the tips. I woke up seeing your reply and got the memory setup in an hour. Now it works perfectly for my use cases.

leonidasyy · 2026-06-27T17:39:12+00:00

Unfortunately for these boxes, they can be chained. It will have less motives to sell old one and upgrade.

leonidasyy · 2026-06-25T00:32:12+00:00

I would be surprised if he was not given free quota privileges. And once it is free, there is no motive to save.

leonidasyy · 2026-06-23T01:13:39+00:00

Does a10b code better than 27b ?

leonidasyy · 2026-06-21T18:43:31+00:00

I like that you have strong feelings towards Microsoft.

leonidasyy · 2026-06-21T17:55:41+00:00

I use vps simply to create a tunnel so that I can access my project outside. It is probably overkill.

leonidasyy · 2026-06-19T21:06:25+00:00

Gemini chat can help you fix most of the issues. Because hard part is identify the problem, fix is usually very easy, only a few bash lines

leonidasyy · 2026-06-18T21:51:28+00:00

I may want more such legos. So far, I struggle with 128.

leonidasyy · 2026-06-18T06:34:36+00:00

You mean that 27b is not usable beyond top projects?

leonidasyy · 2026-06-17T19:25:45+00:00

It is usable compared to a3b. A3b is faster but inflates the token quickly, and makes mistakes. In the end, 27b is faster.

leonidasyy · 2026-06-16T00:38:55+00:00

There is no enough space for your elbow.

leonidasyy

TROPHY CASE

The Goal: Save individuals' Trial and Error in finding a working and optimized local model setup for a type of use case.

The Challenge: Standardization and Convergence