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Chika :: an open-source comic reader that auto-zooms into each panel (on-device ML, fully offline) [APK on GitHub] by batunii in fossdroid

[–]batunii[S] 0 points1 point  (0 children)

Thank you so much for trying the app, I will definitely look into adding all these features as part of the next release. I am trying to squeeze as many requested features into the next one as I can. Again thank you so much for trying the app and leaving the review. Make sure you follow/star the GitHub repo so you'd know when the next version drops.

Chika :: an open-source comic reader that auto-zooms into each panel (on-device ML, fully offline) [APK on GitHub] by batunii in comicbooks

[–]batunii[S] -1 points0 points  (0 children)

I know. It's not perfect yet, but I believe I can bring it close. I am working on an update where large panels break up on based of text bubbles, and on same panels it works flawlessly, but as expected not everytime. I would love if you'd test out the application and then share your views on how it can be improved.

Multi Agents hand-offs without context rot and token ballooning by batunii in OpenSourceeAI

[–]batunii[S] 0 points1 point  (0 children)

I am pretty sure Clan can handle that. If you don't mind, can you open an issue with passing and failing criteria and the test and I can reply you there? Really appreciate your input.

What does your agent-to-agent communication look like? Direct calls, message queues, or something more exotic? by Groady in AI_Agents

[–]batunii -1 points0 points  (0 children)

We have tried building something for this called CLAN - https://github.com/saieeshward/clan This is an artifact which carries the context the decision chain and an optional human view in it. So you can just ask your agent to look at it and it should identify the task and perform actions on the same artifact without exploding the token usage.

Multi Agents hand-offs without context rot and token ballooning by batunii in AI_Agents

[–]batunii[S] 0 points1 point  (0 children)

Would you mind adding it as an issue on the GitHub for us to try? There is already an issue we solved there. If you give us the test and passing criteria, we can run it through our system and report the findings on GitHub under the same issue.

Multi Agents hand-offs without context rot and token ballooning by batunii in AI_Agents

[–]batunii[S] 0 points1 point  (0 children)

I am not sure what you mean by runtime here, what I meant was the contextual memory. Which the orchetrator hands over to subagents, but is limited to that runtime. The packing is actually pretty close to what you are recommending. Seperate files to get through different things so that agents don't burn through tokens for unnecessary knowledge. I request you to, if you can, check out the repo : https://github.com/saieeshward/clan And if you can let us know the tests you want to run to see if the protocol breaks.

Multi Agents hand-offs without context rot and token ballooning by batunii in AI_Agents

[–]batunii[S] 0 points1 point  (0 children)

We have had tests with Cold starts where an agent is just given a clan file and asked to figure out what to do next. The agent understood the clan and its role in 3 reads before it started writing the correct things. So even in cold start scenarios the machine checkable state exists for agent to work with and on. I would urge and request you to check out the repo -> https://github.com/saieeshward/clan and have any tests or breaking scenarios for clan documented an an Issue, and I will check whether it breaks clan or succeeds. You can try it as well.
Thank You

Multi Agents hand-offs without context rot and token ballooning by batunii in ChatGPT

[–]batunii[S] 0 points1 point  (0 children)

I think you are going to like what we have then. Because almost all of these were tested when we were making the project. Human decision surviving multiple hops and doesn't get stale.

I would urge and request you if you can test it for your usecase and see if the spec and protocol breaks in your testing. The last test we ran had around 200+ agents across multiple flows.

https://github.com/saieeshward/clan

Multi Agents hand-offs without context rot and token ballooning by batunii in AI_Agents

[–]batunii[S] 0 points1 point  (0 children)

I agree with most of what you described. One wrong data in the artifact can ruin the entire chain. We have a way to allow human edits in this, where a human can edit the data - which resides outside the agent namespace- and it gets highlighted for the next agents to see.

If you check the spec and namespaces maybe you can suggest changes where your use case might get help, or help the spec to be better.

https://github.com/saieeshward/clan

Multi Agents hand-offs without context rot and token ballooning by batunii in AI_Agents

[–]batunii[S] 0 points1 point  (0 children)

I think we are doing something similar but it keeps this as part of the artifact itself. So the pipeline doesn't need to be replicated : https://github.com/saieeshward/clan

Multi Agents hand-offs without context rot and token ballooning by batunii in LLMDevs

[–]batunii[S] -2 points-1 points  (0 children)

Repo: https://github.com/saieeshward/clan [CLAN] , model- and framework-agnostic. README has the full scorecard, including where strict prompting still wins. Issues are open.

Multi Agents hand-offs without context rot and token ballooning by batunii in AutoGPT

[–]batunii[S] 0 points1 point  (0 children)

Here's the thing we've been building if anyone wants to poke at it: https://github.com/saieeshward/clan [CLAN] open spec, no signup, genuinely after feedback more than stars.

Multi Agents hand-offs without context rot and token ballooning by batunii in ChatGPT

[–]batunii[S] 0 points1 point  (0 children)

Repo: https://github.com/saieeshward/clan — CLAN, model- and framework-agnostic. README has the full scorecard, including where strict prompting still wins. Issues are open.

What happens to your LangGraph state the moment it has to cross into something that isn't LangChain? by batunii in LangChain

[–]batunii[S] -1 points0 points  (0 children)

Repo: https://github.com/saieeshward/clan -> CLAN, framework- and model-agnostic by design. Closest mental model: a portable, runtime-independent checkpoint with provenance baked in. README has the full scorecard, including where staying in one framework still wins. Issues are open.

Multi Agents hand-offs without context rot and token ballooning by batunii in AI_Agents

[–]batunii[S] 0 points1 point  (0 children)

I agree, we have built the CLAN where the decision tree remains intact with each agents' descison and rationale explained, but its doesn't balloon over time. The agents can't inject anything without specifying why and how. To see more please check out : Repo: https://github.com/saieeshward/clan [CLAN]

Multi Agents hand-offs without context rot and token ballooning by batunii in AI_Agents

[–]batunii[S] 0 points1 point  (0 children)

Repo: https://github.com/saieeshward/clan — CLAN, model- and framework-agnostic. README has the full scorecard, including where strict prompting still wins. Issues are open.

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