DIGNALEGI

Worth reading. A personal index selected by Ratipcan Uysal.

Product Discovery

A personal, scored reading index

A focused reading list

Discovery should reduce uncertainty, not produce theatre.

Start here for practical arguments about customer evidence, opportunity selection, experiments, and learning before a team commits to building the wrong thing well.

Product Discovery / From the index

How I Find Problems to Solve as a Staff Engineer

lalitm.com · Lalit Maganti via Lalit Maganti July 2026

Why read thisStaff-level problem finding starts with repeated organizational friction, then tests whether patterns reveal a real root problem.

Some background helpful · Summary available

Open reading room
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. Full text is available, but the experience is mainly from infrastructure and developer tools at large companies.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
A cutaway camera reveals the mechanism beneath its surface.
02

How Superhuman Built an Engine to Find Product/Market Fit

Why read thisRahul Vohra treats product-market fit as a measurable loop: find users closest to love, then remove blockers for adjacent users.

Summary available

First Round Review via Sachin Rekhi’s PM canon
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. The evidence is sampled with gaps, though the provided excerpts still substantiate the central framework and examples.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
03

Babe Ruth and Feature Lists

Why read thisRanked feature lists can hide whether users mean a nice-to-have improvement, a severe defect, or an urgent reliability failure.

Summary available

Ken Norton — Bring the Donuts via Sachin Rekhi’s PM canon
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. Evidence is complete enough, but it is a single product anecdote rather than a broad framework.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
04

AI Evals: A Hands-On Guide for Product Teams

Why read thisAI evals measure acceptable output in probabilistic workflows, using context-specific correctness, recurring error patterns, and baselines.

Some background helpful · Summary available

Product Talk — Teresa Torres September 2026
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. The evidence is sampled with gaps, so completeness and all examples cannot be fully judged.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →

Product Discovery archive

Filter by score
70+

11 pieces · Highest scores first.

05

Product Managers are More Valuable and Less Protected Than Ever

Why read thisAnand and JZ argue that cheap building shifts product management from roadmap ownership to problem definition, context, and disciplined learning.

Summary available

Reforge July 2026
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. The article is an event recap and includes course promotion, but the provided text gives enough substantive argument and examples.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
06

This is the Product Death Cycle. Why it happens, and how to break out of it

Why read thisChen argues that weak traction can trigger a feature-building loop that mistakes user requests for the real growth bottleneck.

Summary available

andrewchen.com via Editor’s archive · Refind August 2023
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. The provided text appears substantive, though some extracted boilerplate disclosures are unrelated to the article.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
07

What is Good Product Strategy?

Why read thisMelissa Perri treats product strategy as disciplined learning toward outcomes, not a feature roadmap pretending uncertainty has disappeared.

Summary available

melissaperri.com via Sachin Rekhi’s PM canon
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. Evidence appears substantive, but visual elements and any material outside the supplied text were not evaluated.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
08

The Politics of Pilot Teams

Why read thisPilot teams work as visible political proof that empowered product teams can deliver outcomes before a large organization transforms.

Some background helpful · Summary available

Silicon Valley Product Group · Marty Cagan July 2025
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. The supplied evidence is substantive but does not show empirical data, counterexamples, or independent validation beyond the author's product-model framing.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
09

Opportunity Solution Trees: Visualize Your Discovery to Stay Aligned and Dr

Why read thisProduct discovery stays clearer when outcomes, customer opportunities, candidate solutions, and assumption tests remain separate instead of collapsing into feature debate.

Some background helpful · Summary available

Product Talk — Teresa Torres via Editor’s archive · Pocket
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. Evidence is sampled with gaps, so completeness, repetition, and the full quality of the guide cannot be judged confidently.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
10

From the Product Discovery index

The Art of Product Management in the Age of AI

Why read thisSachin Rekhi argues AI may pull product management back from coordination tax toward vision, strategy, design, execution, and human taste.

Some background helpful · Summary available

Sachin Rekhi May 2026
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Historical score; not verified under the current evidence-review process.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →
11

How we use friction logs to improve products at Stripe

Why read thisFriction logs preserve the live journey: intent, sequence, failures, artifacts, and disciplined observations before feedback hardens into detached complaint.

Some background helpful · Summary available

mikebifulco.com via Editor’s archive · Pocket
A personal relevance score

80–100: high value. 70–79: worth the time. Below 70: below the usual publication threshold.

Evidence-reviewed score based on available publisher text. Evidence supports a practical framework, but the contribution appears more operational than conceptually deep from the supplied text.

Scores reflect one reader’s profile, not an objective quality rating. Best is a separate personal selection.

How scoring works →