rishabh
1.8K posts


@LokeshVirat18K Under Armour
it’s comfortable af and handles sweat quite well
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we’re creating a monster

Miranda Nover@mirandanover
she says this one is better, and it got 500 likes in her Facebook group
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@eastdakota Thanks for the incident write up.
As someone in the outage business (handling not causing) it represents a standard all should aspire to
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Not my fault.
Park City Transit@ParkCityTransit
Our @transitapp is currently experiencing technical disruptions, impacting real time bus information. We apologize for the inconvenience & are working on getting this back online. Our busses are still running as scheduled. Only real time reporting is impacted by this outage.
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@ErnestoSOFTWARE I supported Hindi in my project, which had tons of content, so it was costly. Ironically the only people who paid anything from that region - they all had English locale on their devices.
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@kieranselfapps @ErnestoSOFTWARE majority of smartphone+internet users in India also have no issue speaking & understanding English
so language isn’t really a problem for apps, but it’s a very unique and hard to crack market for b2c apps for a multitude of other reasons
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@ErnestoSOFTWARE I’m wondering how many more consumers this would open an app up to in India. Would an app using English still be popular, or would a lack of localisation make it tank.
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@ShreeshKaushik @TrulyMonica @MEAIndia Indian founders have tried, there’s too much redtape for it to succeed.
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@TrulyMonica @MEAIndia We need a Palantir for India. A deeply technical company providing real time insights, intelligence and inputs to help the Govt. identify threats, national security breaches etc.
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This guy booked a flight, straight reached some random poop festival and is now milking it to malign India for almost a week now. If he ever gets a visa again, joke would be on you @MEAIndia

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rishabh retweetou

why is it so hard to get support from @frido_official 😭
2 weeks since I placed an order and there's literally zero comms. Their support # doesn't even work & WhatsApp support just says escalated, please wait...
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rishabh retweetou

OK, so as someone who works on this stuff daily, here's my take on LLMs for coding: There are some things they're really good at and some things they're really bad at, and knowing where to draw the line on how to use them is the key.
I remain astounded by how incredible LLMs are for my productivity. I have to manage multiple engineers and write a ton of code on a daily basis. AI just makes my life a lot easier, because of three things:
1) It's really good at writing boilerplate code that you *need* (read and write to this AWS resource, create an SFTP client for me, etc). It's also extremely good at doing regimented tasks with minimal complexity (generate test cases, etc).
2) It's extremely good at finding (coding) packages that you didn't know existed. Python, Java etc are just oceans of libraries at this point, and the documentation for a lot of them is fairly sparse and shoddy, and exists as a Frankenstein's Monster across multiple websites and forums. No sane person has the time to read this.
3) It's amazing at helping you debug — whether through parsing large volumes of logs, or through processing errors for complex pipelines and suggesting solutions. You have to be pretty careful with prompts here, but if you know how to prompt it, the gains are immense. Things that used to take me 5-6 hours to debug now sometimes take minutes.
BUT: it's really bad at one major thing.
AI is horrible — and I mean *HORRIBLE* — at generating complex pieces of code and logic. A hallmark of AI-generated code is that it yields insanely long and unnecessarily convoluted blocks, with a ton of emojis.
If you ever ask it to do something open-ended and semi-complex, be ready for your work to swell by 5x. (In the process, it'll also generate a couple very nasty bugs.)
I'm pretty okay with senior, staff and principal software engineers using a lot of AI to speed them up. At this point, those folks have been coding at a high level for years, and it's second nature for them. Exceptions always exist, but they're generally very aware of where it can go wrong, and how.
I'm less patient with inexperienced engineers doing this, because they tend to blindly use AI to generate volumes of code and think "Lots of Code = Good". What this usually results in is bugs that eventually get exposed, which the rest of us then have to clean up. Moreover, they don't learn much from this, and so they're not building any muscle memory or making any real progress.
Nate Silver@NateSilver538
I'm just one person, and my programming needs are somewhat unusual (building various kinds of statistical forecasting models). But I'm just not seeing the consistent productivity gains from LLMs that I would have expected if you'd asked me 6 months ago.
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Using Postgres as a Data Warehouse
- Start with Postgres 18+ — asynchronous I/O makes table scans 2-3x faster than Postgres 15
- One command runs everything: `docker-compose up`. If partitioning breaks on localhost, it'll break in prod — test the real structure first
- Async I/O in Postgres 18 changes everything — sequential scans that took 45 seconds now take 15
- No config changes needed — it just works faster out of the box
- Postgres isn't just storage — it's your transform layer, your cache, your query engine
- Materialized views = dashboards that don't run live queries when 500 people open Slack at 9 AM
- Partition by date or tenant — keeps queries under 3 seconds without bigger hardware
- VACUUM and ANALYZE aren't optional
- Use schemas like folders — `raw` for ingestion, `staging` for transforms, `analytics` for BI
- JSONB feels flexible until you try to aggregate Millions rows — use real columns for anything you'll query often
- Foreign keys and constraints catch bad data before your dashboard does
- DuckDB reads Postgres tables directly — `duckdb 'SELECT * FROM postgres_scan(...)'`
- Run heavy aggregations in DuckDB, write results back to Postgres — best of both worlds
- Postgres 18's async I/O + DuckDB's columnar engine = the fastest local analytics stack nobody talks about
- Indexes win 90% of performance battles — btree for filters, GIN for arrays, BRIN for time-series logs
- `EXPLAIN ANALYZE` until you understand how Postgres thinks — if it scans 5M rows, add an index
- Async I/O helps, but indexes help more — fix the query plan before throwing hardware at it
- Backup is boring by design: `pg_dump` to S3 every night
- Back up schemas separately from data — schema recovery is 10x faster than full restores
- Postgres 18's faster I/O means backups and restores complete in half the time
- The real test: can a new engineer clone your repo, run `docker-compose up`, and query prod-like data in 5 minutes?
- Postgres 18 is the warehouse you already have — just use it properly
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rishabh retweetou

@thekitze @factoryai droid + cursor
claude code is great, but the randomly changing limits are a bummer
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@boringmarketer llm.txt doesn't even have any benefits
structured content > everything else for all things A/G/S EO
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rishabh retweetou

early bird gets the worm.
well here's something that has been brewing for a while.
as someone who has worked in early stage companies all my life.
early stage marketing faces a fundamental issue, the issue being that startups focus on it too late in their building journey. startups are setting themselves up for failure by not doing marketing early.
which is why i've joined @early_partners where i'm going to work with @gautxm and @trippyhippy on insititutionalising fractional marketing for early stage companies and helping early stage companies grow with marketing strategy
Early joins the @TalentedAgency grid of companies.
i'm super excited to be on this journey and can't wait to see what the future holds.
if you'd like to know more. check us out below
early.partners
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