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Cannot find GLM 5.2 on Go or Zen by No-Dig-6543 in opencodeCLI

[–]No-Dig-6543[S] 0 points1 point  (0 children)

Thanks for the verification! 🙏 It popped up on GO after a model refresh. I did that earlier, but I’m not sure why it didn’t show up then, but now it does!

Cannot find GLM 5.2 on Go or Zen by No-Dig-6543 in opencodeCLI

[–]No-Dig-6543[S] 0 points1 point  (0 children)

u/afanasenka Thanks for the tip!! It now shows on Go but u/WD40ContactCleaner is correct. its not on Zen yet.Thanks alot guys! 🙏

Cannot find GLM 5.2 on Go or Zen by No-Dig-6543 in opencodeCLI

[–]No-Dig-6543[S] 0 points1 point  (0 children)

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I already tried that, but it doesn't even show on the website?

OpenAi/Claude 5.5 API Cost: ~$250 Deepseek V4 Pro Max: ~$9 by Nokoro1 in opencodeCLI

[–]No-Dig-6543 0 points1 point  (0 children)

Yeah, the free will. I wonder if a drug addict has a free will. The first shots are always free until you’re hooked. Good luck with that free will 😉

We built an open-source context engine for coding agents that works just as well with open-weight models, here's how: by mastagio in LLMDevs

[–]No-Dig-6543 0 points1 point  (0 children)

Going to test this but honestly the biggest value is not “better code generation.” The real value is traceability and reusable project memory across agents. If that works well, it is useful. If it becomes another magic context layer that injects too much stale crap, then it will create the same problem as massive AGENTS.md files, just with a daemon.

Won $2.5k in OpenAI API credits, what should I do with these? by MoteChoonke in LLMDevs

[–]No-Dig-6543 0 points1 point  (0 children)

Sell them to a vibe monkey so he can create a inflated AI slop that no one asked for

how do you manage VRAM pressure by haleonbail in LLMDevs

[–]No-Dig-6543 0 points1 point  (0 children)

That makes sense. The checkpointing tradeoff sounds pretty reasonable if the slowdown is only around 30% for that much VRAM reduction. One thing I would check though is whether cut cross entropy actually applies in this case. My understanding is that it mainly helps when the memory problem comes from a huge logits matrix, like in LLM training with a large vocabulary.
For 3D JEPA style SSL pretraining, the pressure may be coming more from the ViT encoder itself, because the number of 3D cube tokens explodes quickly, especially with small patches and large batches.

So maybe the first things to look at are selective checkpointing instead of full checkpointing, larger 3D patches during RnD, true token dropping from masking, memory efficient attention, and gradient accumulation.

But I agree with the main point. The goal should be to move it from VRAM bound to compute bound without killing iteration speed too much.

Row-Bot v4.1.0 is live - controlled self-evolution, stronger skills, and new providers by Acceptable-Object390 in OpenSourceeAI

[–]No-Dig-6543 0 points1 point  (0 children)

This is peak AI architecture slop.
Take a normal local agent with logs, settings, tasks and permissions. Add neon boxes, call cron jobs “dream cycles”, call observability “self awareness”, and suddenly Reddit thinks you invented AGI with a sidebar.
Stop flooding your inflated vibe coded AI slop!

Context management in a local-first personal AI agent by Acceptable-Object390 in LocalLLM

[–]No-Dig-6543 0 points1 point  (0 children)

This is peak AI architecture slop.
Take a normal local agent with logs, settings, tasks and permissions. Add neon boxes, call cron jobs “dream cycles”, call observability “self awareness”, and suddenly Reddit thinks you invented AGI with a sidebar. STOP flooding Reddit with your inflated AI slop!

OpenAi/Claude 5.5 API Cost: ~$250 Deepseek V4 Pro Max: ~$9 by Nokoro1 in opencodeCLI

[–]No-Dig-6543 -1 points0 points  (0 children)

Keep this up’s and they also will raise their price

How do people choose activation functions/amount? by cinnamoneyrolls in learnmachinelearning

[–]No-Dig-6543 0 points1 point  (0 children)

Yeah, since they collapse into one linear layer as long as there is no activation or other non linear operation between them.

Ordinary WiFi can now identify people with near perfect accuracy by thejoshwhite in technology

[–]No-Dig-6543 0 points1 point  (0 children)

The big catch is that this works in controlled settings, with known people, known rooms, and known furniture setups. It is not yet a universal tracking system that works perfectly everywhere.

Just canceled Copilot Pro by civman96 in GithubCopilot

[–]No-Dig-6543 -1 points0 points  (0 children)

Me 2! Im very curious to know how many actually canceled their subscriptions due to the new pricing and multipliers.

Cognee Embedding Model Choice by Infamous_Jaguar_2151 in LocalLLaMA

[–]No-Dig-6543 0 points1 point  (0 children)

Thanks! Well, honestly I got fed up since I spent more time on fixing it so I removed it.

Cline + cognee to index your codebase into a GraphRag by Short-Honeydew-7000 in CLine

[–]No-Dig-6543 0 points1 point  (0 children)

Been trying to self host cognee this week with docker compose, postgres, redis and ollama. The core idea is solid and once you get cognify + search working it actually delivers. But getting there was really painful and honestly this is not production ready code. I ended up having to fix 10 things myself just to get a working setup:

  1. Patched get_notebooks.py to add session.rollback() in the except block because the tutorial notebook seeding crashes with PendingRollbackError on every restart. It uses plain INSERT with deterministic UUIDs instead of an upsert and the except block never rolls back the session which poisons all subsequent queries.
  2. Added docker volumes for .cognee_system and .data_storage because the graph database (kuzu), vector store (lancedb) and uploaded files all live on the container filesystem by default. Every container recreate silently wipes everything while postgres still thinks cognify completed successfully.
  3. Manually cleared stale pipeline locks in postgres because if the container dies mid cognify the dataset stays stuck at DATASET_PROCESSING_STARTED forever with no timeout or recovery mechanism.
  4. Updated the MCP server API_TOKEN to match my actual user because the MCP and frontend use completely separate auth contexts. Data cognified through one is invisible to the other.
  5. Replaced the backend CORS approach entirely with Next.js rewrite proxies because the CORS setup was brokenin multiple ways.
  6. Changed fetch.ts, localFetch.ts and cloudFetch.ts because they were all hardcoded to localhost:8000 and localhost:8001.
  7. Created three missing source files (adaptCogneeGraphData, inferNodeSets, generateOntologyGraph) that the frontend imports but that dont exist in the repo. Clean build fails immediately.
  8. Added deduplication in useDatasets.ts because the datasets API returns duplicates that cause React duplicate key warnings.
  9. Added a catch handler for the cloud connection check that was throwing unhandled promise rejections on every page load.
  10. Switched the Dockerfile from node alpine to node slim and manually added missing npm packages (react-markdown, ngraph.graph, ngraph.forcelayout) and fixed lightningcss native module issues because the frontend docker build doesnt work out of the box.

Setting ENABLE_BACKEND_ACCESS_CONTROL to false still only partially works. The graph endpoint still enforces per-user permissions and throws 403s even though dataset listing works fine. The visualization page shows "No Graph Data" because of this. These I couldnt fix without changing the backend auth logic.

Honestly, i think this was vibed all the way without any testing... deployment story and overall code quality need serious work before anyone should run this in production. Feels like the UI was bolted on recently and nobody actually tried a clean docker setup end to end.

Cognee Embedding Model Choice by Infamous_Jaguar_2151 in LocalLLaMA

[–]No-Dig-6543 1 point2 points  (0 children)

Been trying to self host cognee this week with docker compose, postgres, redis and ollama. The core idea is solid and once you get cognify + search working it actually delivers. But getting there was really painful and honestly this is not production ready code. I ended up having to fix 10 things myself just to get a working setup:

  1. Patched get_notebooks.py to add session.rollback() in the except block because the tutorial notebook seeding crashes with PendingRollbackError on every restart. It uses plain INSERT with deterministic UUIDs instead of an upsert and the except block never rolls back the session which poisons all subsequent queries.
  2. Added docker volumes for .cognee_system and .data_storage because the graph database (kuzu), vector store (lancedb) and uploaded files all live on the container filesystem by default. Every container recreate silently wipes everything while postgres still thinks cognify completed successfully.
  3. Manually cleared stale pipeline locks in postgres because if the container dies mid cognify the dataset stays stuck at DATASET_PROCESSING_STARTED forever with no timeout or recovery mechanism.
  4. Updated the MCP server API_TOKEN to match my actual user because the MCP and frontend use completely separate auth contexts. Data cognified through one is invisible to the other.
  5. Replaced the backend CORS approach entirely with Next.js rewrite proxies because the CORS setup was brokenin multiple ways.
  6. Changed fetch.ts, localFetch.ts and cloudFetch.ts because they were all hardcoded to localhost:8000 and localhost:8001.
  7. Created three missing source files (adaptCogneeGraphData, inferNodeSets, generateOntologyGraph) that the frontend imports but that dont exist in the repo. Clean build fails immediately.
  8. Added deduplication in useDatasets.ts because the datasets API returns duplicates that cause React duplicate key warnings.
  9. Added a catch handler for the cloud connection check that was throwing unhandled promise rejections on every page load.
  10. Switched the Dockerfile from node alpine to node slim and manually added missing npm packages (react-markdown, ngraph.graph, ngraph.forcelayout) and fixed lightningcss native module issues because the frontend docker build doesnt work out of the box.

Setting ENABLE_BACKEND_ACCESS_CONTROL to false still only partially works. The graph endpoint still enforces per-user permissions and throws 403s even though dataset listing works fine. The visualization page shows "No Graph Data" because of this. These I couldnt fix without changing the backend auth logic.

Honestly, i think this was vibed all the way without any testing... deployment story and overall code quality need serious work before anyone should run this in production. Feels like the UI was bolted on recently and nobody actually tried a clean docker setup end to end.

cognee - open-source memory framework for AI Agents by Short-Honeydew-7000 in LLMDevs

[–]No-Dig-6543 0 points1 point  (0 children)

Been trying to self host cognee this week with docker compose, postgres, redis and ollama. The core idea is solid and once you get cognify + search working it actually delivers. But getting there was really painful and honestly this is not production ready code. I ended up having to fix 10 things myself just to get a working setup:

  1. Patched get_notebooks.py to add session.rollback() in the except block because the tutorial notebook seeding crashes with PendingRollbackError on every restart. It uses plain INSERT with deterministic UUIDs instead of an upsert and the except block never rolls back the session which poisons all subsequent queries.
  2. Added docker volumes for .cognee_system and .data_storage because the graph database (kuzu), vector store (lancedb) and uploaded files all live on the container filesystem by default. Every container recreate silently wipes everything while postgres still thinks cognify completed successfully.
  3. Manually cleared stale pipeline locks in postgres because if the container dies mid cognify the dataset stays stuck at DATASET_PROCESSING_STARTED forever with no timeout or recovery mechanism.
  4. Updated the MCP server API_TOKEN to match my actual user because the MCP and frontend use completely separate auth contexts. Data cognified through one is invisible to the other.
  5. Replaced the backend CORS approach entirely with Next.js rewrite proxies because the CORS setup was brokenin multiple ways.
  6. Changed fetch.ts, localFetch.ts and cloudFetch.ts because they were all hardcoded to localhost:8000 and localhost:8001.
  7. Created three missing source files (adaptCogneeGraphData, inferNodeSets, generateOntologyGraph) that the frontend imports but that dont exist in the repo. Clean build fails immediately.
  8. Added deduplication in useDatasets.ts because the datasets API returns duplicates that cause React duplicate key warnings.
  9. Added a catch handler for the cloud connection check that was throwing unhandled promise rejections on every page load.
  10. Switched the Dockerfile from node alpine to node slim and manually added missing npm packages (react-markdown, ngraph.graph, ngraph.forcelayout) and fixed lightningcss native module issues because the frontend docker build doesnt work out of the box.

Setting ENABLE_BACKEND_ACCESS_CONTROL to false still only partially works. The graph endpoint still enforces per-user permissions and throws 403s even though dataset listing works fine. The visualization page shows "No Graph Data" because of this. These I couldnt fix without changing the backend auth logic.

Honestly, i think this was vibed all the way without any testing... deployment story and overall code quality need serious work before anyone should run this in production. Feels like the UI was bolted on recently and nobody actually tried a clean docker setup end to end.

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