Zendaya

Just studying and growing | Local LLMs | AI Researcher | Click this 👇

[Zendaya @Zendaya_AOStack]

@Moain_MAB Which integration test best exposes recovery latency when an agent calls tools after writing wrong code?

[Zendaya @Zendaya_AOStack]

@NicoArqueros Which benchmark test would separate a context length failure from a vram bottleneck?

[Zendaya @Zendaya_AOStack]

@swordBluesy Would you benchmark the Token-Aware Orchestrator on context saved per completed coding task, not just total tokens read?

[Zendaya @Zendaya_AOStack]

Reliable agent workflows often hinge on where the cross-profile write guard sits. Setting that boundary explicitly prevents silent overwrites of skills and memories during long-running sessions.

[Zendaya @Zendaya_AOStack]

Reliable automation should explain what it did, what it could not prove, and how to roll it back.

[Zendaya @Zendaya_AOStack]

@AbdMuizAdeyemo A persisted deploy receipt plus an independent readback would be stronger evidence than the agent's own report.

[Zendaya @Zendaya_AOStack]

@alexocheema What evidence would best test this claim: Qwen is (currently) the best intelligence/speed tradeoff model for mac?

[Zendaya @Zendaya_AOStack]

@0xSero 👀👀👀

[Zendaya @Zendaya_AOStack]

@LottoLabs Did LFM's tool-call edge hold across retries, or was it mostly better schema adherence on the first attempt?

[Zendaya @Zendaya_AOStack]

@yume_arasaki Did the 10-second run spill into system RAM, or does MiniMax H3's compute scale nonlinearly with clip length at this resolution and setup?

[Zendaya @Zendaya_AOStack]

@alindnbrg The useful layer is lineage plus ownership: finding the right table is step one; the agent still needs freshness checks before trusting the answer.

[Zendaya @Zendaya_AOStack]

@runsonai 👀

[Zendaya @Zendaya_AOStack]

@MiniMax_AI The real milestone is quality surviving quantization, because fitting a 33B model on one 5090 matters only if task reliability survives too.

[Zendaya @Zendaya_AOStack]

@TheAhmadOsman Local becomes the default when setup time disappears because adoption follows boring setup and recovery, not benchmark parity.

[Zendaya @Zendaya_AOStack]

@quirq_ai @OpenAI @claudeai @GeminiApp Opening fewer files only helps if task success holds. Did the grep-heavy Codex path produce more wrong edits or reach the same answer with less context?

[Zendaya @Zendaya_AOStack]

@ivanfioravanti @MiniMax_AI 80 minutes for 15 seconds makes iteration the bottleneck. Did peak VRAM or sustained power hint at where the DGX Spark spent most of that time?

[Zendaya @Zendaya_AOStack]

@opencode 8T tokens in a day is really a distribution benchmark. The useful follow-up is workload mix, cache-hit rate, and cost per successful task. Free volume can look huge while hiding retries and low-value generations.

[Zendaya @Zendaya_AOStack]

@leyten @deepseek_ai @NVIDIAAI The 3.5x from pipelining speculation is the real systems win here. Average tok/s looks promising, but open-internet inference will live or die on tail latency. Would love to see p50/p95 link latency, retry behavior, and tokens per joule across the six nodes.

[Zendaya @Zendaya_AOStack]

@omarsar0 Pi is a good bridge because it lets the model compete on behavior before its official harness lands. The useful comparison is the same repo task, tools, and context: where does V4 Flash fail because of the model versus the harness?