Ever see p50 hold steady while p95 climbs? That widening tail can reveal provider degradation before errors rise. Compare model/day latency in Tempr Gateway. #LLMOps #AIOps https://temprhq.io/gateway
@temprhq.bsky.social
@temprhq.bsky.socialEver see p50 hold steady while p95 climbs? That widening tail can reveal provider degradation before errors rise. Compare model/day latency in Tempr Gateway. #LLMOps #AIOps https://temprhq.io/gateway
@temprhq.bsky.socialA provider can look like the right default until pricing changes the decision. Compare model pricing across providers in TemprHQ, then bring the chosen key into your team’s IDE, terminal, or API. #AIDev #LLMOps https://temprhq.io/providers
@temprhq.bsky.socialDid your fallback chain actually reduce errors? Compare before-and-after rates in Tempr Gateway, then segment by key and model to see where failures moved. #LLMOps #AIOps https://temprhq.io/gateway
@temprhq.bsky.socialA model can look reliable because fallbacks rescue it—while its errors trigger retries and extra spend. Compare model error rates with fallback activity in request logs before changing client settings. #LLMOps #BYOK https://temprhq.io/gateway
@temprhq.bsky.socialSeeing a virtual key burn through quota? Check request logs for repeated failures clustered by timestamp before changing client retries. #LLMOps #BYOK https://temprhq.io/gateway
@loopandretry.bsky.socialContext engineering: what fits and what gets dropped
"Context engineering" is getting used as a rebrand for prompt writing, and that undersells it into uselessn…
https://loopandretry.github.io/posts/context-engineering-what-fits-what-gets-dropped/?ref=bluesky
@temprhq.bsky.socialFor stable prompts, try response caching before changing models or routing. Use request logs to compare latency and verify the impact. #BYOK #LLMOps https://temprhq.io/gateway
@tmtabor.ioI also appreciate the fine-grained control over the context window. Commands like /clear and /compact are a nice touch, and something that is usually a black box in most other harnesses. #LLMOps
@tmtabor.ioWhat has your experience been with MCP? Are you finding ways to manage these risks in production? #LLMOps
@umbra-codex.bsky.social
@ombulabs.aiYou do not have to hand-roll spans. One package plus an OTLP endpoint gets a full LLM trace into Phoenix or Langfuse without touching your agent code.
@loopandretry.bsky.socialHow indirect prompt injection actually happens
Every indirect prompt injection follows the same four-stage pipeline — placement, retrieval, interpretation, execution — an…
https://loopandretry.github.io/posts/how-indirect-prompt-injection-happens/?ref=bluesky
@klannu.bsky.social📅 Session Details
✔ Trainer: Mr. Arpan Ghosh
✔ Date: Sep 19th, 2026
✔ Time: 8:30 AM IST
🔗 Join the Live Session
✔ Link: l1nk.dev/5wpoebj
✔ Meeting ID: 414 582 207789771
✔ Passcode: Gd2Dz63e
Call: +91 7032290546
Website: www.visualpath.in/generative-a...
@it-path-solutions.bsky.socialThe 2am Stack Trace Nobody Can Read
itpathsolutions.substack.com/p/the-2am-st...
#n8n #AIAgents #LLMOps #DevOps #Automation #MLOps #IncidentResponse #NoCode #BuildInPublic
@paul-crinigan.bsky.socialYour model returned something confident, well formatted, and wrong. Nothing in the stack objected.
Guardrails are code around the model, not a model feature. Cheap regex first, classifiers only on what survives.
@paul-crinigan.bsky.socialYour LLM app returned 200 OK, normal latency, no exception. The answer was still wrong.
APM cannot see that. It shows up only when you trace prompts, retrieved context and token cost per request.
@mlcon.bsky.social🚀 AI demos are easy. Reliable AI systems are not.
At #MLcon Berlin, 5 sessions tackle evals, latency, context engineering & production-ready agents.
📍 Berlin | Nov 16–20, 2026
@gradientflow.comGenerative AI deployment has crossed a threshold 🙌 it's no longer just a machine learning problem. It's a distributed systems problem. Abi Aryan unpacks what that really means for engineering teams. #LLMOps #AIEngineering #GenerativeAI
thedataexchange.media/abi-aryan/