MacBook Pro with M5 Pro and M5 Max

Claude's Cycles [pdf]

Motorola GrapheneOS devices will be bootloader unlockable/relockable

Don't become an engineering manager

GPT‑5.3 Instant

Intel's make-or-break 18A process node debuts for data center with 288-core Xeon

When AI writes the software, who verifies it?

Voxile: A ray-traced game made in its own engine and programming language

Nobody Gets Promoted for Simplicity

An Interactive Intro to CRDTs (2023)

TikTok will not introduce end-to-end encryption, saying it makes users less safe

We've freed Cookie's Bustle from copyright hell

Textadept

TorchLean: Formalizing Neural Networks in Lean

Weave – A language aware merge algorithm based on entities

Hey HN - we&#x27;re Tarush, Sidhant, and Shashij from Cekura (<a href="https:&#x2F;&#x2F;www.cekura.ai">https:&#x2F;&#x2F;www.cekura.ai</a>). We&#x27;ve been running voice agent simulation for 1.5 years, and recently extended the same infrastructure to chat. Teams use Cekura to simulate real user conversations, stress-test prompts and LLM behavior, and catch regressions before they hit production.The core problem: you can&#x27;t manually QA an AI agent. When you ship a new prompt, swap a model, or add a tool, how do you know the agent still behaves correctly across the thousands of ways users might interact with it?
Most teams resort to manual spot-checking (doesn&#x27;t scale), waiting for users to complain (too late), or brittle scripted tests.Our answer is simulation: synthetic users interact with your agent the way real users do, and LLM-based judges evaluate whether it responded correctly - across the full conversational arc, not just single turns.
Three things make this actually work:
Scenario generation + real conversation import - Our scenario generation agent bootstraps your test suite from a description of your agent. But real users find paths no generator anticipates, so we also ingest your production conversations and automatically extract test cases from them. Your coverage evolves as your users do.Mock tool platform - Agents call tools. Running simulations against real APIs is slow and flaky. Our mock tool platform lets you define tool schemas, behavior, and return values so simulations exercise tool selection and decision-making without touching production systems.Deterministic, structured test cases - LLMs are stochastic. A CI test that passes &quot;most of the time&quot; is useless. Rather than free-form prompts, our evaluators are defined as structured conditional action trees: explicit conditions that trigger specific responses, with support for fixed messages when word-for-word precision matters. This means the synthetic user behaves consistently across runs - same branching logic, same inputs - so a failure is a real regression, not noise.Cekura also monitors your live agent traffic. The obvious alternative here is a tracing platform like Langfuse or LangSmith - and they&#x27;re great tools for debugging individual LLM calls. But conversational agents have a different failure mode: the bug isn&#x27;t in any single turn, it&#x27;s in how turns relate to each other. Take a verification flow that requires name, date of birth, and phone number before proceeding - if the agent skips asking for DOB and moves on anyway, every individual turn looks fine in isolation. The failure only becomes visible when you evaluate the full session as a unit. Cekura is built around this from the ground up.
Where tracing platforms evaluate turn by turn, Cekura evaluates the full session. Imagine a banking agent where the user fails verification in step 1, but the agent hallucinates and proceeds anyway. A turn-based evaluator sees step 3 (address confirmation) and marks it green - the right question was asked. Cekura&#x27;s judge sees the full transcript and flags the session as failed because verification never succeeded.Try us out at <a href="https:&#x2F;&#x2F;www.cekura.ai">https:&#x2F;&#x2F;www.cekura.ai</a> - 7-day free trial, no credit card required. Paid plans from $30&#x2F;month.We also put together a product video if you&#x27;d like to see it in action: <a href="https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=n8FFKv1-nMw" rel="nofollow">https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=n8FFKv1-nMw</a>. The first minute dives into quick onboarding - and if you want to jump straight to the results, skip to 8:40.Curious what the HN community is doing - how are you testing behavioral regressions in your agents? What failure modes have hurt you most? Happy to dig in below!

Launch HN: Cekura (YC F24) – Testing and monitoring for voice and chat AI agents

California's Digital Age Assurance Act, and FOSS

Graphics Programming Resources

LLMs can unmask pseudonymous users at scale with surprising accuracy

My spicy take on vibe coding for PMs