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Rediflow – SSR project management, one source of truth, no spreadsheet

Hacker News

Rediflow – SSR project management, one source of truth, no spreadsheet

I wanted to try AI-assisted software development, so I built Rediflow: a server-rendered project management app. The kind of thing that would have been useful around the turn of the millennium — when plans lived in one spreadsheet, capacity in another, and everyone kept asking "where am I allocated?" — but didn't really exist in a simple, integrated form. I spent many evenings and weekends building it. It's done* now, and I enjoyed making it. Core features: Single source of truth — Project flow, portfolio flow, and capacity in one place. No Excel sprawl. Update once, flows everywhere — Change a figure once; it propagates to charts, reports, and views. Demand chart — Project plan (PROJPLAN), Estimated (EST), and Actual as monthly bars and cumulative lines. Work packages, tasks, milestones, deliverables — All in one project view. Capacity dashboard — Demand vs capacity across projects and people. My allocations — Staff see their rolling 12 months of capacity and project assignments. Audit logging — Who changed what and when. Data quality checks — Built-in validation. Schema migrations — Alembic, version-controlled schema. Backup procedures — Documented (pg_dump, volume backup, WAL). Self-hosted — Podman or Docker. Stack: Flask + PostgreSQL 18+, server-rendered, minimal JS. OIDC (Authentik) optional. Docs: https://rediflow_eu.gitlab.io/rediflow · Repo: https://gitlab.com/rediflow_eu/rediflow Self-financed, no external funding or benefits received. * Yeah, software is ready when it is so decided. Until the next revisit.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: dock, single, tasks · Missing: mac, agents, macos
65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, ide, io · Missing: https docs, excited, just released
58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive
36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month, monthly · Missing: mobile apps, ios, personal
35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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