Asher Atlas

Problem

We were launching the first-ever music livestreaming commerce platform with no users, no infrastructure, and no playbook. Midway through, load testing I initiated revealed the platform would collapse under festival traffic — and leadership didn't want to slow down to fix it.

The Hard Call

I had to choose between staying quiet and hoping the load wouldn't materialize, or persistently escalating until I changed the roadmap. I built a case with data, found internal allies, and kept raising the issue in every forum until I won the reprioritization.

What I Did

• Shipped the 0→1 platform: concept → 100+ live events with integrated ticketing, merch, and artist tooling
• Initiated load testing against stakeholder traffic estimates — found a 2K ceiling with 8K expected at peak
• Built the internal case for reprioritization and drove alignment against executive resistance to delay new features
• Led the infrastructure scaling work alongside engineering, increasing capacity 4x before the festival deadline

Problem

Board game groups have run these trades since 2007 on a tool veterans tolerate and new people don't finish. The matching was fine.

The Hard Call

A new front on the old tool would still have required a BoardGameGeek account. That was the wall. The matcher stays on the organizer's laptop.

What I Did

• Finding a game and deciding what you'd give for it are different jobs, so Browse and My Wants are different screens.
• A newcomer, someone who already knows the old tool, and the organizer get different controls in the same product. The server decides what participants can see of each other.

Problem

Board game recommendations are genuinely hard. Player constraints are complex and contextual ("something for five people, not too long, my group hates trading"). LLMs hallucinate on incomplete metadata. And users don't trust suggestions that feel mechanically correct but miss the actual situation. Generic AI recommendations weren't solving this.

The Hard Call

After shipping V1, I discovered two wrong assumptions at once. The engine was matching on keywords but not reasoning — it couldn't interpret the intent behind a contextual request, only filter on surface constraints. And my entire feedback loop was built on power users, not the casual players I was designing for, so I'd been optimizing for the wrong problems. Rather than patch either one, I rebuilt both: the architecture and the validation methodology.

What I Did

Architecture

• V1: built a five-node hybrid pipeline combining deterministic constraint filters with LLM result formatting; shipped and tested it.
• Identified that V1 matched on surface keywords but did not reason through tradeoffs in a complex, contextual request.
• Ran a structured six-model evaluation before committing to V2 and selected on failure-mode severity rather than benchmark scores.
• Added a dedicated Intent Interpreter to reason through requests before passing them to the matching pipeline.
• Built a multi-model metadata-enrichment pipeline to clean inconsistent catalog fields and reduce hallucinations at the reasoning stage.

Problem

AI resume tools optimize for polished similarity. That creates two trust failures: they can invent or inflate experience, and they can make every candidate sound interchangeable. Product managers need tailoring that reads the role intelligently without turning adjacent experience into expertise they do not have. I also needed the product to work repeatedly for real applications, not just produce one impressive demo.

The Hard Call

The first tailor could produce a strong resume in one model call, but it asked the model to select evidence, rewrite bullets, frame the candidate, and fit the page at once. During dogfooding, one thin but relevant experience could pull the entire resume toward a manufactured specialist identity. I could keep adding prompt rules, or change the system so the failure was harder to create. I chose the architectural reset: separate selection from writing, anchor both to a stable candidate identity, and move mechanical constraints into code.

What I Did

Product strategy

• Narrowed the private-beta wedge to reliable resume tailoring and onboarding; kept Jobs, Pipeline, and Interview Prep hidden rather than treating built code as launch-ready product.
• Turned founder dogfooding issues into a lane-based roadmap so urgent applications did not erase launch-critical work.
• Defined honesty as the product differentiator: every claim should be traceable, reviewable, and interview-defensible.
• Designed onboarding around an early reviewable artifact instead of a long AI interview, so a user who pauses still leaves with something useful.

Agent architecture

• Split the system into construction-time candidate modeling and a repeated per-job pipeline: Coach → evidence libraries and identity prior; Analyzer → real must-haves and fit read; Tailor → evidence selection, deterministic assembly, and constrained writing.
• Replaced free-form bullet rewriting with select-by-ID from a verified library. Selected bullets remain verbatim; the generative surface is primarily title and summary.
• Built deterministic A/B/C/F fit scoring from model classifications, distinguishing transferable competencies from non-transferable thresholds.
• Added provenance and approval states so extracted or inferred material stays staged until it is reviewed or explicitly promoted.
• Used code for metric grounding, structural checks, deduplication, page-fit budgets, and other rules that do not require language judgment.

Evaluation and economics

• Built a 13-job-description regression set with founder-labeled directional fit expectations and explicit bad-role traps.
• Requested a 12-model analyzer bake-off, compared 11 completed model responses, and selected by failure severity, cost, and threshold judgment rather than generic benchmark ranking.
• Rejected an image-context optimization after an N=5 test showed that the honesty-critical trap passed only 1/5 times with image delivery versus 5/5 with text.
• Split the tailor between a low-cost selector and a higher-quality constrained writer, reducing the measured path from about $0.042 for the preceding all-Sonnet rank-select call to about $0.019 cold / $0.013 warm per tailoring.
• Ran four synthetic candidate personas through onboarding and tailoring. No invented roles or experience were detected; the tests exposed unsupported metric transformations, duplicate role grouping, pseudo-STAR stories, and stale-profile handling, which became concrete fixes.