We just launched HeyMilo Red. heymilo.ai/red A standalone cheat detection layer that analyzes recordings for AI-assisted answers, coaching, second screens, proxy candidates and synthetic media, then returns timestamped evidence for human review. API-first and built to work with existing interview workflows.

There are at least two versions of "the model got the answer right." It produced the expected output in a test, or it behaved correctly inside the actual workflow. We care much more about the second.

We've found that production voice systems expose the long tail very quickly. Background noise, accents, interruptions or latency aren't separate problems. The difficult cases are combinations of them happening at the same time.

We spend a surprising amount of time thinking about "reasonable human behavior." A candidate pausing for 15 seconds might be thinking, reading notes, having connection problems or getting help. Systems that interact with humans have to deal with ambiguity rather than pretend it doesn't exist.

Interview integrity should produce hard evidence, not just vibes. "Risk score: 82" is vague and hard to act on. "03:42-04:18: off-screen reading, cadence shift, corroborating transcript pattern" gives a reviewer something they can inspect and act on. That's why we built Red

Seven of 50 tasks scored zero for every model. Not syntax failures but coordination failures: delayed state, event delivery, systems that disagree about what happened. The leading model still left 31 points on the table for work a competent integration engineer finishes routinely.

The biggest gap wasn't model quality. It was surface type. Polling tasks averaged 63.7 out of 100. Webhook tasks averaged 22.8. A 41-point drop across all 20 models. Verify signatures, handle duplicates, reconcile missed events: the code can look done while the vendor log shows duplicates.

Most coding benchmarks grade whether local tests pass. Integration work fails somewhere else: another company's API, webhook redelivery, stale docs, HTTP 200 with an error in the body. We built a benchmark around live ATS vendor simulators and graded from their traffic logs, not the agent's repo.

We tested 20 AI coding agents on 50 ATS integration tasks against live APIs. Fable 5.1 scored 69.3 out of 100. None cleared 70. Today we're releasing Integration Bench: integrationbench.com

We're launching something juicy tomorrow! 👀

Some exciting news! We've raised $6 million in funding, led by @Category_VC with participation from @canaanpartners, @alumniventures, and @remarkable_vc Also launching today: three new agent types. 1. Candidate Recommendations resurfaces talent already sitting in your ATS. 2. AI Scenario Assessment measures how candidates work with AI, built for data annotation, technical, and AI-adjacent roles. 3. Notetaker captures structured notes from live recruiter-led interviews so every candidate interaction produces consistent, reviewable signal. We've screened 1M+ candidates on the platform, 4x revenue in six months, and have brilliant customers like Randstad, Wilson, and Neo Financial in production today. Now back to building! Read the full announcement: heymilo.ai/blog/heymilo-rais…

Is your hiring strategy future-proof? 🚀 Insights reveal AI is no longer just assisting recruitment – it's driving it. Discover what this means for talent leaders: forbes.com/sites/keithferraz… #AI #Recruiting #FutureofWork #TalentAcquisition

AI is no longer just for tech giants; it's transforming small businesses in significant ways.  From streamlining operations to enhancing customer experiences, AI is becoming a vital tool for SMBs to stay competitive. Want to learn more about how AI is impacting small businesses? Check out this article from All Business: allbusiness.com/ai-applicati…