jaakl

There are a few fundamental reasons: 1. at least iOs app privacy/sandboxing model makes sharing any data files pretty difficult, if impossible. I suspect Android has similar issue. 2. there is no such thing as "just map data" - every app has to do opinionated decision what they need in their maps data (e.g. aerials, elevation, choice of POIs, languages etc), how it gets styled (it makes requirements to actual data). And this is for just simple base map data, you have also routing, address search databases etc on top. So there is not much to share really, and not easy to share. Not even talking about huge challenge to standardize it. (I'm founder of Nutiteq, we also did FOSS SDKs for offline maps)

Author notes. Motivation and target: country detection can be certainly solved with standard tools eg PostGIS, with 3 prerequisites: 1) you know the stack (right formats, resolutions, CRSs, have operational database or certain suitable library properly setup), 2) you have good vector data 3) enough compute for potentially non-trivial task. With this domain-specific solution none of the three is needed - you just import a library (Python and JS provided) and call one method. It is compact (sub-MB datasets for minimal use in eg web browser, for much better accuracy optional ~10MB download) and order(s) of magnitude faster than generic point-in-polygon SQL queries (benchmarks are provided): ~1M lookups per second. No online, no DB servers needed etc.

AI reminds me a lot https://en.wikipedia.org/wiki/TRIZ , it is like a good machine implementation of it.

Is there a browser plugin enabling to swap out any parts of a website/app with own ones? With prompt/vibe-based input, so any user can customize websites in any imaginable way. Maybe it should.

What developers and fellow Product Managers think of the attempt to redefine Scrum to be (even) better fit for the AI-empowered teams? And how these teams would be different?

Semi-academic attempt to build semantic-level standard system for data type classification. Useful for metadata semantics data lakes / warehouses beyond basic physical level like text/number, with assumption that it may be useful for AI-targeted metadata, your text to sql cases. https://github.com/jaakla/semantic-field-types

Reactor h2o itself does not carry radiation, but any extra molecules in tend to do it, thats the reason why the water is as clean you can get, over-distilled. This by itself means that it is not potable (btw for disposal to environment it gets re-salinated), so they told the story of professor drinking it must be an urban myth. It is bad even for skin expose (swimming in it), but hopefully that worker got just a few seconds expose and is well. Source: training trip in a nuclear center.

And “chicken” in which one?

Not just energy cost, but also licensing cost of all this content…

ASCII tabulature was not invented by ChatGPT, it is decades old thing. It is easier to write with basic computer capabilities, and also read for ChatGPT (and humans with no formal music education), so it is probably even more prominent in the Internet than "standard graphical notation". So it quite expected that LLMs have learned a lot of that.

Note that being Estonian OÜ (LLC) brings convenience of both having fully electronic communication towards any state affairs and also super easy to get (no even registration needed) yearly financial reports. Actually more-less the only touchpoint with state is the yearly report, no taxes until you have salaries, apply for VAT, deal with licensed area or really cash out the profits. Also you can be foreign, "e-resident" to use such OÜ.

The official company reporting source is https://ariregister.rik.ee/eng/company/16225385/Organic-Maps... . Yearly PDF reports are in Estonian language, but your favorite AI should help. The numbers are in actual EUR (not housands), so they seem to have 33KEUR profits, IMHO no huge piles of money to worry too much for.

I hate it. I used it to have carefully curated metadata (sources etc) to my collection of tens of tables, and someone else took backup/restore of the database and all this was lost.

It seems to be based on very common naive belief that things which are named same or similar in different domains are conceptually same, so "lets deduplicate" ? There can be rare moments when they really are, but then the moment passes and then you only have troubles.

Did you try to put all this (complex and external) context to the context (claude.md or whatever), with intructions how to do proper TDD, before asking for the tests? I know that may be more work than actual coding it as you know all it by heart and external world is always bigger than internal one. But in long term and with teams/codebases with no good TDD practises that might end up with useful test iterations. Of course developer commiting the code is anyway responsible for it, so what I would ban is putting “AI did it” to the commits - it may mentally work as “get out of jail card” attempt for some.

Some models do have US 2025 president election results explicitly given in system prompt. To fool all who use it for cutoff check.

My main takeaway here is that the models cannot tell know how they really work, and asking it from them is just returning whatever training dataset would suggest: how a human would explain it. So it does not have self-consciousness, which is of course obvious and we get fooled just like the crowd running away from the arriving train in Lumiére's screening. LLM just fails the famous old test "cogito ergo sum". It has no cognition, ergo they are not agents in more than metaphorical sense. Ergo we are pretty safe from AI singularity.

Well “building” is also troubleshooting, fixing a problem. Just in a bit more general level: ideally it is not fixing a “small” well-defined problem in software, but bigger and fuzzier problem in the real world: the thinking process and tooling is quite the same. Of course many devs dont think of it like that, they just try to fulfill given requirements without understanding real problem they troubleshoot. Actually a lot of software “builds” are really troubleshooting attempts on top of other software also, which makes that border even fuzzier.

Not funny anymore, after 1/20/2025

One more zoom LoD please: to the actual pages of the books!

I’m doing datalake modernization for medium-large enterprise and spent last months in sales calls of MS Fabric vs Snowflake vs Databricks. All fun, but now with the managed Iceberg in AWS (S3 tables) I tend to consider to choose none of them: just plain Iceberg is good enough. Of course someone needs to write and read it; but there are so many good free options already, even build does not feel scary. So I would go to the short side in Snowflake in medium-long term (looking their current value prop at least). Databricks has maybe more future as it has ML/AI-first approach. In short term we might still start with SF (with its Iceberg features), as the alternative future stack needs to mature and establish a bit.

This one from 2010 is just creepy: https://news.ycombinator.com/item?id=1027093

Does it respond to http queries?

It seems Claude (3.5 Sonnet) provided the longest summary for this discussion using basic single shot prompt for me:

After reviewing the Hacker News thread, here are some of the main repeating patterns I observed:

* Fatigue and frustration with AI hype: Many commenters expressed being tired of the constant AI hype and its application to every domain. * Concerns about AI-generated content quality: There were recurring worries about AI producing low-quality, generic, or "soulless" content across various fields. * Debate over AI's impact on jobs and creativity: Some argued AI would displace workers, while others felt it was just another tool that wouldn't replace human creativity and expertise. * Skepticism about AI capabilities: Several commenters felt the current AI systems were overhyped and not as capable as claimed. * Copyright and ethical concerns: Many raised issues about AI training on copyrighted material without permission or compensation. * Polarized views on AI's future impact: There was a split between those excited about AI's potential and those worried about its negative effects. * Comparisons to previous tech hypes: Some likened the AI boom to past technology bubbles like cryptocurrency or blockchain. * Debate over regulation: Discussion on whether and how AI should be regulated. * Concerns about AI's environmental impact: Mentions of AI's large carbon footprint. * Meta-discussion about HN itself: Comments about how the discourse on HN has changed over time, particularly regarding AI. * Capitalism critique: Some framed issues with AI as symptoms of larger problems with capitalism. * Calls for embracing vs rejecting AI: A divide between those advocating for adopting AI tools and those preferring to avoid them.

These patterns reflect a community grappling with the rapid advancement and widespread adoption of AI technologies, showcasing a range of perspectives from enthusiasm to deep skepticism.

In short: wordpress.org is just a personal homepage of Matt Mullenweg, not legally or financially related to neither Foundation nor Automattic? There could be technical relations but who has not "forgotten" your personal homepage to your employee's machine, just a honest mistake.

Thanks for the writeup. There are indeed use cases, especially in the MS multiverse. Proof of the none->basic->complex “can do everything” (soap,xml,rpc)->radically simpler “do what really matters” (rest, json, markdown) path. I’m not really sure if dbt semantic layer is the final open “standard” for the future analytical models and metrics, it has own questionmarks, it is literally just a transformer with metrics as addon and there are just initial implementations, but today I’d rather give that thing a try. Simpler is so much better

The problem of semantic models what I've seen in tools like Looker, Tableau and Qlik (very probably same for PowerBI) is that they are tightly coupled to the tool itself, work within them only. Now you want "modern data system" then you want them decoupled and implemented with an open semantic model which is then accessable by data consumers in Google spreadsheets, Jupyter notebooks and whatever BI/Analytics/reporting tools your stakeholder uses or prefers. There are very new solutions for this like dbt semantic models; their only issue is that they tend to be so fresh that bigger orgs (where they do make most sense) may be shy on implementing them yet. To the original topic - not sure how much PG17 can be used in these stacks, usually much better are analytical databases - BigQuery, Snowflake, maybe Redshift, future (Mother)Duck(db)

Yep it is not to OP really. Just observation how programming numbers works nowadays: I had my first useful miniprogram using about 3-4 cryptic bytes about 33 years ago, now you can make a playable game with <200 chars of human language. It is of course “compiled” to megabytes with a tera+byte scale interpreter, but meaningful source is in human scale (again).

In nutiteq mobile maps SDK (later Carto, now abandonware) we used specifically compressed bitmap to represent 'water' and 'empty land' tilemasks to cover these two special cases. We provided planet-scale mobile embedded mbtiles package in 30GB if I remember well. This tile mask (quite instant bitmap index) concept should work well for server case also.

Why so many bytes? I wrote one using just 141 bytes and it took just few seconds to write, and it is the first functional game I've ever written). Result: https://claude.site/artifacts/3b35069f-4d51-4415-9f58-69988c...

Sweden is building now new prison for underage and lacks thousands of seats for inmates. Other countries are talking about renting their spare capacities to them.