@temprhq.bsky.socialWhat happens when your preferred model is unavailable? Write the path now: fallback model, provider, key owner, and switch trigger. Tempr helps teams compare supported models and pricing. #AIDev #LLMOps https://temprhq.io/providers
@nftdemon.comCrypto in. Tokens out. No KYC theater.
DemonRoute keeps inference simple: anonymous keys, OpenAI-shaped API, models that ship.
#Inference #LLMOps #DemonRoute #CryptoBilling #SaaS
brought to you by NFT Demon Holdings LLC
https://demonroute.com
@nftdemon.comCrypto in. Tokens out. No KYC theater.
DemonRoute keeps inference simple: anonymous keys, OpenAI-shaped API, models that ship.
#Inference #LLMOps #DemonRoute #CryptoBilling #SaaS
brought to you by NFT Demon Holdings LLC
https://demonroute.com
@nftdemon.comCrypto in. Tokens out. No KYC theater.
DemonRoute keeps inference simple: anonymous keys, OpenAI-shaped API, models that ship.
#Inference #LLMOps #DemonRoute #CryptoBilling #SaaS
brought to you by NFT Demon Holdings LLC
https://demonroute.com
@temprhq.bsky.socialHow much does a successful response cost in staging vs production? Prefix virtual keys with stg- and prod-, then compare Gateway logs by key and model. #BYOK #LLMOps https://temprhq.io/gateway
@temprhq.bsky.socialA single dev environment can burn through a shared provider budget. Set a quota on its virtual key, keep production separate, and watch usage by key. #BYOK #LLMOps https://temprhq.io/gateway
@temprhq.bsky.socialRotating a provider key? Keep clients unchanged: add the replacement, test through Gateway, switch traffic, monitor logs, then revoke the old key. Virtual keys preserve the same endpoint. #BYOK #LLMOps https://temprhq.io/gateway
@temprhq.bsky.socialWhich requests deserve attention first? Group Gateway data by model or virtual key, then compare p95 latency with error rate. The high-high group is both slow and likely to fail. #LLMOps #AIEngineering https://temprhq.io/gateway
@iam.slys.devDify including observability in the first description is the tell. Once workflows, retrieval, and models interact, failure analysis becomes part of the product. Any production-ready AI stack that treats visibility as optional is still demo tooling. #Observability #LLMOps
@temprhq.bsky.socialWhat happens when your primary model goes down—or access disappears? Document the trigger, fallback provider/model, key owner, scope, and verification step before the incident. #LLMOps #AIInfrastructure https://temprhq.io/providers
@temprhq.bsky.socialA monthly model review beats a reactive provider switch. Check the models your team depends on, compare provider pricing in TemprHQ, and document whether the default still fits. #AIDevelopment #LLMOps https://temprhq.io/providers
@temprhq.bsky.socialWho owns the key after the workflow is shared? Check the owner, storage, scope, rotation plan, and revocation path before connecting a provider across your IDE, terminal, or API. #AIDev #LLMOps https://temprhq.io/providers
@sarubot.bsky.social
@aipromptingclinic.bsky.socialGemini 3.5 Flash Lite Prompts: XML scaffolding and 2-shot compact exemplars achieve 99.4% schema accuracy with sub-250ms latency. Full engineering guide: https://aipromptingclinic.com/gemini-prompts/gemini-3-5-flash-lite-prompts #GeminiAI #LLMOps #PromptEngineering
@loopandretry.bsky.socialGoal drift: how a multi-agent crew ends up solving a different problem
Loop drift is one agent staying busy without getting closer to done.
https://loopandretry.github.io/posts/multi-agent-goal-drift/?ref=bluesky
@temprhq.bsky.socialIs your provider slow, or is one workflow spiking? Compare p50 with p95: a high p50 means broad slowness; a wide gap means occasional tail latency. #LLMOps #AIInfrastructure https://temprhq.io/gateway
@temprhq.bsky.socialWhich model is actually cheapest after failures? Compare cost per successful response by model using Gateway logs, then weigh errors and latency before changing providers. #LLMOps #BYOK https://temprhq.io/gateway
@temprhq.bsky.socialHow long has your model shortlist stayed accurate? Make it a quarterly checklist: recheck reference cost, context window, and required capabilities for every workflow, then replace models that drift out of fit. #AIModels #LLMOps https://temprhq.io/models
@praveenlavu.bsky.socialEvery AI output: approved. Three batches, green across the board. I should have felt relieved. Instead I felt the floor tilt. A gate that never closes is not a gate. https://praveenlavu.com/dispatch/the-test-that-has-to-fail/ #LLMOps
@sarubot.bsky.social