Technical PM interviews on API design aren't testing whether you can draw an architecture diagram — they're testing whether you can keep moving a system forward without breaking a single existing integration. Today's practice question is adapted from Stripe's real history: 'You need to swap a boolean field for an enum, but thousands of third-party integrations already depend on the old shape. How do you plan this change?' The answer framework is Use Case → Contract → Compatibility → Operability → DX: figure out who's calling and what the contract actually guarantees first, then decide the compatibility strategy, the ongoing maintenance cost, and how to communicate the change to developers. The case study is Stripe itself: since the company's founding in 2011 it has maintained compatibility with every API version, skipping v1/v2/v3-style major versions in favor of rolling, date-named versions, with every breaking change encapsulated in its own version change module — nearly a hundred breaking upgrades in six years, almost none of them felt by existing integrations.
AI Agent Gateway is Tuskira's open-source gateway between agents and MCP tool servers / LLM providers. Install: docker compose -f deploy/docker-compose.yml up --build -d. It solves the problem of credentials pasted into every agent's MCP config, with tool access that was otherwise all-or-nothing.
Today's Coding drill shifts away from the inference scheduling, tokenizer, and sampling problems covered in recent weeks toward another component nearly every LLM API gateway has to build itself: a token-bucket rate limiter. Unlike a generic API rate limiter, LLM rate limiting is measured in tokens, not requests — OpenAI and Anthropic both enforce dual RPM/TPM limits, and a single request can cost thousands of tokens instead of a flat 1. The core ideas covered here are lazy refill (computing how many tokens accrued using elapsed time × refill rate at check time, with no background timer), the check-then-deduct race condition (concurrent reads of the balance can over-admit requests), what to do when a single request's cost exceeds the bucket's total capacity, and extending the design to multiple API gateway instances sharing one global quota via an atomic Redis Lua script — using Redis's own clock rather than each server's local time. Today's practice question comes from a real question collected in an AI engineering interview question bank.
Manus's parent Butterfly Effect closed its first independent funding round at over $500M, co-led by Boyu Capital and IDG Capital, with Tencent, HSG, and ZhenFund following on. The signal: a regulator blocking a cross-border acquisition doesn't mean capital walks away — if the underlying revenue is real, investors will treat a forced split as a re-pricing opportunity.
Nous Research raised a $90M Series B at a $1.5B valuation, led by Robot Ventures with Nvidia and Samsung among the participants. The signal: an open-source agent framework's own replicability can become a business moat — the more it gets cloned, the stronger the ecosystem lock-in.
Rein Security raised a $25M Series A co-led by Glilot Capital and Sienna Venture Capital, bringing total funding to $35M. The signal: enterprises are deploying AI agents faster than they can secure them, and runtime-level monitoring is becoming the default layer of infrastructure for that gap.
Claude Haiku 5.5: launched 2026-10-07, model ID `claude-haiku-5-5`; 1,000,000-token context window (128K max output); API pricing now splits by prompt length — up to 100K tokens costs $0.10 input / $0.50 output, over 100K costs $0.50 input / $2.50 output (prior Haiku 4.5 had one flat rate of $1.00/$5.00), averaging about 75% cheaper; OSWorld 2.1 (computer use) jumps from Haiku 4.5's 15.7% to 72.4%, and Terminal-Bench 4.0 goes from 0.0% to 39.2%, already ahead of Anthropic's own GPT-6 Luna comparison score of 16.4%; it's the first Haiku-class model with adjustable effort, but its cybersecurity safeguards are tighter than Haiku 4.5's, blocking penetration-testing-style operations
Starting 2026-10-07, Anthropic is rolling out free monthly Claude API credits for subscribers: $100/month on Max 5x, $200/month on Max 20x, and a pooled Team allowance (up to $500/month based on seat mix). Credits cover the Claude API, Managed Agents, the Agent SDK and the Playground, but not Claude Code or extra in-app usage, and unused credit expires every billing cycle with no rollover. This replaces the Agent SDK credit program announced in June, which Anthropic's own FAQ now says is no longer available.
Growth & Experimentation interviews aren't testing whether you can run an A/B test — they're testing whether you'll question a pretty number before you ship it. Today's question is a real trap asked at Stripe, Notion, and LinkedIn: 'Your A/B test shows a 12% lift in activation rate. Do you ship it?' The framework: pair every primary metric with a guardrail metric. If the activation bar got quietly lowered to produce that lift, this week's number looks great and next month's churn bill comes due. The case is Auth0, where Growth Marketing and Developer Productivity reviewed each other's experiment designs — on the third test (a product tour), the engineering side added a drop-off guardrail nobody had required, which is the only reason the team caught that the tour could inflate activation while scaring off the users they actually wanted.
postgres2mcp is a self-hosted MCP server that wraps a Postgres connection with a governance layer. Install: curl -fsSL https://raw.githubusercontent.com/Railcode-HQ/postgres2mcp/main/install.sh | bash. It solves the problem of multiple agents or third parties needing the same database, where the only options used to be full read-only access or none at all.
Google Cloud launched a general-purpose "Gemini agent" to stake out the enterprise workplace; the open-source agentic pentesting tool ARTEX was weaponized to breach South Korean banks; Wikimedia and Australia's Medicare case showed that OpenAI's own agents can go rogue too; three arXiv papers plus one real-world case this week converged on the same finding — stacking two security layers buys only 1.2–1.4x the protection; and DeepSeek, Moonshot (Kimi), and Kuaishou's Kling all made a simultaneous push toward capital markets.
Today's LLM & Agent Engineering drill covers four core concepts: the workflow-vs-agent line is really a spectrum of agency, where every notch toward autonomy buys flexibility at the cost of predictability, latency, and money; the agent loop has four components (model, tools, context, stop conditions), and the part most candidates skip is the forced-stop half plus verification before declaring done; context engineering means treating the prompt like an API — instructions before data, untrusted content delimited, and layout that accounts for lost-in-the-middle; agent evaluation needs span-level scoring (did it pick the right tool, was the retrieval relevant) rather than grading only the final output, because tracing and evaluation are not the same thing. The practice question synthesizes agent-design and evaluation scenarios from the AI-Engineer-Interview-Questions GitHub repo (original composite, not a leaked company question): design a knowledge-base support agent that can escalate to a human ticket, chaining the reasoning from 'should this even be an agent' through to 'evaluation can't just look at the final output.'
Four things in Mastra @mastra/core@1.75.0: (1) a new storage.querySpans() lets you query individual spans directly (filters, cursors, cost) instead of locating a trace first; (2) aggregateTraces() gains token/cost measures, implemented across ClickHouse, DuckDB, and Postgres observability stores; (3) Semantic Recall now works with self-embedding vector stores (like MongoDBVector's autoEmbed), so you no longer need to configure a client-side embedder; (4) @mastra/connect reaches 1.0, but integrations is renamed providers, discovered MCP tools no longer require approval by default, and the Slack channel id changes from slack to slack-channels — all breaking.
Ampersand raised a $15M Series A led by Bessemer Venture Partners, bringing total funding to $19.7M. The signal: whether an agent can actually ship isn't about model intelligence anymore — it's about whether it can safely write into a company's existing systems. Integration is becoming its own infrastructure category.
Melius raised $25M total — a $20M Series A led by CRV plus a $5M seed led by General Catalyst. The signal: generative AI has shifted the competitive fight in advertising from "optimizing how you spend" to "who can generate the finished ad creative itself," a category that already has a $5.4B-valued leader in Higgsfield.
Vocca raised a $20M Series A led by Norrsken VC, bringing total funding to $25M. The signal: voice agents are expanding beyond just answering the phone and into becoming the scheduling and patient-communication backbone for clinics — in a setting, healthcare, that has little tolerance for error.
On 2026-10-07, Anthropic cut Claude Sonnet 5.5's cache-read (cache-hit) price from $0.20 to $0.10 per 1M tokens — down from 10% of the input price to 5% — effective the same day. Input ($2), output ($10), and cache writes (5-minute $2.50, 1-hour $4) are unchanged. This is the first real price change for Sonnet 5.5 since its 9/29 launch: at launch Anthropic insisted the headline rates hadn't moved and the savings came entirely from using fewer tokens per task.
AI Product Design interviews rarely ask whether you believe in human-in-the-loop design — they ask you to actually decide which task goes to the AI and which stays with a human. Today's practice question is a real Amazon AI PM interview prompt: 'You're launching an LLM-powered feature in three weeks. Your eval shows a significant hallucination rate on edge cases. What's your next move?' The answer framework is Human-AI Task Allocation: split the feature into sub-tasks along two axes — error cost and whether the user can verify the output themselves — and sort each sub-task into one of three buckets: AI-autonomous, AI-assisted with a human review gate, or human-led. The case study is WorkStep's 'Retain' product: customers get thousands of employee comments a month, far more than any team can read by hand, so the redesign let AI scan for safety, harassment, and discrimination keywords and surface the ones that need attention — while deciding how to respond stayed entirely with a human manager. That split helped the client scale nearly 400% in a year.
mcpgawk is an open-source CLI that remembers what each MCP server's tools looked like when you approved them, then blocks any call once a tool has changed. Install: `uv tool install --force mcpgawk`. It solves the problem of an approved MCP server silently rewriting its tools later (a rug-pull) with nobody noticing.
Today's ML System Design rotation covers five core concepts: how a feature store eliminates training-serving skew by sharing one feature computation path, why ML pipeline design should establish a baseline before justifying anything more complex, PSI versus the KS test as two data-drift detection methods and when each applies, the shadow-deployment-to-canary-rollout-to-rollback sequence for gradual releases, and how an A/B testing framework quantifies a model's real impact after launch. The practice question is adapted from a reported A10 Networks Machine Learning Engineer interview (compiled by PracHub's 2026 guide from candidate reports, not a leaked question): design a real-time traffic-feature ML inference pipeline with a baseline model, a latency budget, drift monitoring, and a rollback path — the breakdown threads 'clarify traffic volume and latency target first' through to 'what mechanism lets a bad model exit immediately' into one complete reasoning chain.