Reports covers the research of 15K+ AI Agent product, open source project and datasets release in 2026 H1 (Jan-June) from GitHub, HuggingFace, ProductHunt, and community marketplaces to understand where the AI Agent ecosystem is heading in 2026 second Half.
The analysis include topics:
- 2026 H1 AI Agent market by category growth and distribution
- Quantitative analysis Top Agent platforms, domains, features and capabilities
- New Product Hunt launch trends, GitHub ecosystem patterns
- Market opportunities and saturated areas
Yeah, Qwen3.8-Max is the new Flagship model for coding and harness system and many other benchmarks are reaching equal performance as Claude and other close models. That's gonna drop the price of LLM in agent landscape a lot.
Yes That's true. The original MCP server recommend stdio, streaminghttp and other formats and earlier month in 2026, they recommend switch to streaminghttp way which is easier to support higher QPS and distinguish request using individual session ids and multiple connections (But the implementation always give wrong MCP request error).
And now they provide a single HTTP request which make the access easier. Probability because the growing market of skills and clis direct access instead of MCP tax (long prompts) to complete for Agents access plugin market. So basically it might be the results of competition from other alternatives!
So How do you prevent the continuously fine-tunes itself from drifting away from its original capabilities? The idea of training LoRA adapters from user corrections is interesting, and how do you balance personalization vs. catastrophic forgetting?
Well, when fine tuning it doesn't do it as one big adapter. It works like skills it learns a new skill and that becomes an adapter, that is then tested against a golden file that tests its core skills as well as the skills the ai wants to know and if it fails to complete the essential skills than the adapter gets dropped and the new training data considers the gaps in its capabilities.
Hi I just read your methodology and I have a quick question about how the handbook are parsed and feed into the context window? The article mentioned that each handbook contains roughly 8K to 79K tokens of extracted text, and did the harness system use grep or search tools to find relevant chunks and feeds to the context, or did it just feeds all the pdf output to the model? There might by distribution bias between real world tasks e.g. Agents grep keywords from docs and only use the relevant chunks. So all the models of the overall pass@1 is relatively low compared to real world scenarios, that might not be the same precision that user experience when they actually handle the daily task? How did the benchmark bridge the gap?
Yes, The A Slop button is helpful if most of contents are generated by LLM and no real opinions or human knowledge. And that's probability not worth reading at all!
I have a question about how did the project evaluate the technical English performance compared to other skills/MCPs and with LLM w/o skills? In the Github repo, there is a summary of "measured: 6 Claude models × 8 tasks × 2 conditions, 96 runs", Does it means the percentage of violations in the words? If that's the case, the measurement might be a little bit strange weather the STE measure is already in the prompt? STE violations per 100 words ▼ 72.9% (every model won). The bench or evaluation should not be in the same prompt, like eval/test.
I used to intern at Mars chocolate factories supply chain, and are familar with the cost of products such as Ferrero.
1. Overall Inventory Cost: From the supply chain perspective, there might be months ahead of procurement before the actual production of chocolate, so the decrease in cocoa materials will not reflect the overall cost soon and there fluctuations.
2. Raw Cocoa materials costs only takes a few of overall chocolate product costs. The sales, distribution, retails are large portion of the overall cost. For a typical Milk chocolate, the estimated cocoa material cost share of final retail price ~5–15%, Even Dark chocolate with high Cocoas the materials cost will only takes ~15–30% of the overall product prices.
Hi quick question, xfwm4 has long development history. And what strategies do you use when modernizing an old C codebase without introducing regressions?
The analysis include topics: - 2026 H1 AI Agent market by category growth and distribution - Quantitative analysis Top Agent platforms, domains, features and capabilities - New Product Hunt launch trends, GitHub ecosystem patterns - Market opportunities and saturated areas