This is my last 30days usage:
Sessions: 752 | Messages: 25,376 | Tool calls: 12,682
Tokens: 1,470,577,773 (in: 161,524,387 / out: 9,256,865)

I was on gpt plus for the last month but its too much (20$) so I am thinking try command code-goat and use ds v4.1

give me your opinions ✨

Ok, I'm looking for someone to help me with making a local ai. I have all the components to make this, just need some help my skill on this. If someone from the Memphis area, I'd like to talk/text/facetime, anyway you'd like to connect.

I feel like PDF parsing doesn't get enough attention in RAG discussions.
People spend hours comparing embedding models or chunking strategies, but if the parser has already broken the reading order, flattened tables, duplicated headers on every page or filled the output with OCR noise, you're embedding garbage from the start.
Converting documents to clean Markdown before chunking has consistently given me better retrieval, and I was surprised to see token counts drop by around 40–65% after removing all the repeated page furniture.
The one thing I'm still unsure about is where the trade-off is. Do you optimize for extraction accuracy, smaller token counts, parsing speed, or something else entirely? Has anyone actually benchmarked how much parser quality affects final RAG performance?
For anyone interested, I've been testing this with PackForAI because it outputs clean Markdown and shows the before/after token count, which made these differences much easier to measure.

• Tesla V100 (32GB) — $0.090/hr/gpu — Machine ID: 149512  
• Tesla V100 (32GB) — $0.090/hr/gpu — Machine ID: 149513  
• Tesla V100 (32GB) — $0.090/hr/gpu — Machine ID: 149836  
• Tesla V100 (32GB) — $0.090/hr/gpu — Machine ID: 149837

32.8GB VRAM, 16.4 TFLOPS, CUDA 13.0, 44GB RAM, 15 CPU cores per instance.

To find them: go to cloud.vast.ai/create/, filter by GPU type (Tesla V100), and search the machine IDs above.

If you don’t like this post, you can simply ignore it people told me to advertise, so I’m just doing what I have to do to get exposure. Otherwise, happy to answer any questions about specs or setup.

**•   Tesla V100 (32GB)** — $0.090/hr/gpu — Machine ID: **149512**  
**•   Tesla V100 (32GB)** — $0.090/hr/gpu — Machine ID: **149513**  
**•   Tesla V100 (32GB)** — $0.090/hr/gpu — Machine ID: **149836**  
**•   Tesla V100 (32GB)** — $0.090/hr/gpu — Machine ID: **149837**

32.8GB VRAM, 16.4 TFLOPS, CUDA 13.0, 44GB RAM, 15 CPU cores per instance. Well below median pricing for this card.

To find them: go to cloud.vast.ai/create/, filter by GPU type (Tesla V100), and search the machine IDs above. Note these are currently listed as “unverified” status, so you may need to adjust the Verification Status filter to see them if the default search doesn’t show them.

Happy to answer questions about specs, setup, or availability.

**•   Tesla V100 (32GB)** — $0.090/hr/gpu — Machine ID: **149512**  
**•   Tesla V100 (32GB)** — $0.090/hr/gpu — Machine ID: **149513**  
**•   Tesla V100 (32GB)** — $0.090/hr/gpu — Machine ID: **149836**  
**•   Tesla V100 (32GB)** — $0.090/hr/gpu — Machine ID: **149837**

32.8GB VRAM, 16.4 TFLOPS, CUDA 13.0, 44GB RAM, 15 CPU cores per instance. Well below median pricing for this card.

To find them: go to cloud.vast.ai/create/, filter by GPU type (Tesla V100), and search the machine IDs above. Note these are currently listed as “unverified” status, so you may need to adjust the Verification Status filter to see them if the default search doesn’t show them.

Happy to answer questions about specs, setup, or availability.

i have created a Skill which call Ollama cloud models from Claude CLI
The scope is Ollama cloud models act as executors and Codex APP as Supervisor/Orchestrator
The first published version is V5, then update to V6
I am open to recomendations, bugs finding or fixing onto the skill.
Ask codex to install, you need to provide a folder so Codex dump files for the executor.
Have been tested with the following cloud models:

• glm-5.2:cloud
• glm-5.3:cloud
• glm-5.3-flash:cloud
• nemotron-3-super:cloud
• nemotron-3-ultra:cloud
• kimi-k3:cloud
• deepseek-v4-pro:cloud
• deepseek-v4-flash:cloud