DIGNALEGI

Worth reading. A personal index selected by Ratipcan Uysal.

Product-Market Fit

A personal, scored reading index

A focused reading list

Look for customer pull, not ceremonial proof.

A focused collection on recognizing real demand, interpreting retention and customer behaviour, and separating repeatable market pull from hopeful internal narratives.

Product-Market Fit / From the index

The only thing that matters

pmarchive.com via Sachin Rekhi’s PM canon

Why read thisMarket pull, not team or product quality alone, dominates startup outcomes until product/market fit exists.

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. Evidence supports the framework and examples, but not a rigorous empirical proof of causation.

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

Product Work Beyond Product Market Fit

Why read thisPost-product-market-fit work splits into feature, growth, scaling, and expansion problems that need different processes, metrics, risks, and sequencing.

Some background helpful · Summary available

Reforge June 2020
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 the full structure and ending cannot be completely verified.

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

How scoring works →
03

What Are Growth Teams For, and What Do They Work On?

Why read thisGrowth means scaling usage after product-market fit, using product changes to connect users with value that already retains them.

Summary available

Casey Winters 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 long and substantive, but slide visuals and any omitted formatting were not evaluated.

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

How scoring works →
04

Not all good products make good businesses

Why read thisA good product can still fail as a business when problem size, frequency, price, complexity, or founder fit break viability.

Brief pending · original available

intercom.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. The evidence is conceptual and advisory rather than research-backed, but the mechanisms are explicit and well grounded in product practice.

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

How scoring works →

Product-Market Fit archive

Filter by score
70+

8 pieces · Highest scores first.

05

How Supabase became the essential infrastructure for the AI era | Paul Copplestone (Co-founder, CEO)

Why read thisPaul Copplestone frames Supabase’s growth as disciplined sequencing: win developers early, rely on Postgres and open standards, then broaden operations.

Some background helpful · Summary available

First Round Review · First Round Staff June 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 →
06

How Gamma pulled off their AI pivot | Jon Noronha (Co-founder and CPO of Gamma)

Why read thisGamma's pivot worked when generative AI solved the blank-page problem, collapsing onboarding, product value, virality, and willingness to pay.

Summary available

First Round Review · First Round Staff 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. Evidence is sampled interview text, so the full episode’s depth and repetition cannot be fully assessed.

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

How scoring works →
07

How to tell if you have product/market fit

Why read thisProduct-market fit appears in organic demand, word-of-mouth referrals, and retention among early core customers.

Brief pending · original available

merci.medium.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.

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 →
08

Why retention is so hard for new tech products

Why read thisChen argues weak retention usually reflects product-category mismatch and early cohort behavior, not missing notifications or onboarding tweaks.

Some background helpful · Summary available

Andrew Chen September 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. Evidence is sampled with gaps, so completeness, full structure, and omitted arguments cannot be judged.

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

How scoring works →