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Searchlight – Open-source Postgres client for macOS

Hacker News

Searchlight – Open-source Postgres client for macOS

Hi HN, Over the past year, I’ve been building a native MacOS Postgres client for my personal use. While there are plenty of existing clients, I built this because: - No open-source Postgres client matched the smooth UX of tools like Sequel Pro/SequelAce (for MySQL). - I missed the satisfaction of long-term product ownership and iteration—recent work has me jumping between projects. - I’ve been using Postgres more lately and wanted to get hands-on to deepen my knowledge. I also wanted a playground to experiment with client features that would help me on day-to-day. Some I have implemented already: - Hover over a foreign key column to see the linked record in a popover. - Autocomplete lookup for foreign key records when inserting/editing rows. - High-level stats pop-up when hovering over a column. - Contextual “sugar” features (e.g., UUID fields include a button to generate a UUID while editing). - On update/insert failures, it tries to highlight the issue on the problematic column, vs some generic error alert. It’s still very bare-bones and I still use it alongside other tools for features I haven’t implemented (management features for tables/schemas/user), but I’m already using as my main client for 90% of what I work on. I’m sharing here to get early feedback. Mostly trying to determine if more people find value in this project if I keep developing it. ps.: I’m using my personal Apple developer account so I can’t notarize the app with Apple. If you try to install from the GitHub releases page MacOS will warn that it can’t verify the developer identity, so you will need to approve the install on Settings > Privacy, or build from source.

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, apple · Missing: agents, agent, cursor
97%97% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
74%74% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal · Missing: mobile apps, ios, entrepreneurs
39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
12%12% 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.

Correct prediction on native model

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