Show HN: TryPost – open-source Social Media Scheduler https://ift.tt/WL4xHgC

Show HN: TryPost – open-source Social Media Scheduler https://trypost.it/en May 26, 2026 at 12:25AM

Show HN: Write your BPF programs in Go, not C https://ift.tt/wPj5eNI

Show HN: Write your BPF programs in Go, not C https://ift.tt/HIGTJQr May 21, 2026 at 08:25PM

Show HN: I made Pokémon but with real animals in the real world https://ift.tt/XtGNbDr

Show HN: I made Pokémon but with real animals in the real world Firstly, apologies, it's not free. It would be difficult to support this for free, it's a paid game. I will now share the technical details, which will probably be most of interest for HN readers. I previously made a carbon footprint tracking app where you photo objects and it tells you the carbon footprint by using an LLM to estimate the data on the fly, e.g. 32kg CO2e / kg of beef, in the UK. At some point, I realised that it is possible to make a Pokémon-style game, but capturing real animals in the real world. This is now possible because: - image recognition is cheap, i.e. identifying animals, and the models (gpt-4o) can detect a (surprisingly) large number of animals and output their exact species. - LLMs can output a species' full taxonomy, pretty reliably. And, more importantly, they can generate game data quickly, on the fly. It would unfeasible to generate the game sprites (images) for every species (millions, worldwide) and their full evolution chain, e.g. caterpillar, chrysalis, butterfly, ahead of time. I realised it's possible to do this in real time. General game flow: - photo animal - send to gpt-4o - return species - send species to LLM, create evolution chain, plus attributes, types and moves. - in parallel, create sprites. All data is cached. The aim of the game is to build up your team and compete with other players to take over gyms. The game is based in the real world, I had to come up with a way to have health centres and shops. These must both have decent coverage, globally. The solution is health centres are places of worship, e.g. churches, mosques, temples etc and shops are real world grocery stores. Every country as far as I can tell has places of worship, with good distribution, which was surprising. Gyms are located in every park worldwide. Challenges: How to get players outside: - I use openstreetmap for the game map, but I overlay my game design on top of it. - To physically make players go out into nature: I use openstreetmap area types to only allow capturing animals when your GPS location is in natural areas, e.g. woodland, parks etc. The aim of the game is to get you out into nature and appreciating animals. - Level system: The solution I came up with is to set the animal levels based on the proximity to built-up areas, e.g. Every ~500 meters you go away from built-up areas, the animal level bands increase by 5 levels. - It would be expensive to render the entire physical world in my game map, so I instead render the map on the fly, deterministically. I also fetch animal calls in real time so that when they enter battle you hear a pigeon cooing, for example, which is pretty cool. I also fetch the animals conservation status, i.e. how endangered is it, and give you more reward (leaves, in-game currency) for capturing rarer animals. I "launched" the game about a month ago, but have not really been publicising it as I've been working on various updates and improvements, but now I am sharing it more openly. It's got about 20 players so far, from around the world, and around 500 unique animal species have already been encountered. Challenges have been keeping the costs low. Servers cost about $200 / month, text-gen is basically free as I get free tokens from OpenAI for sharing data, it's not privacy-related, and image-gen costs about $0.04 per sprite (2 per animal). My background: not a programmer, originally a mechanical engineer and then business development manager, then started learning programming and building apps with AI in the last few years. Feel free to ask me any technical details, happy to share. https://ift.tt/wOXv9U8 May 25, 2026 at 11:48PM

Show HN: CRED-1 – Open domain credibility dataset for on-device pre-bunking https://ift.tt/pJveMVd

Show HN: CRED-1 – Open domain credibility dataset for on-device pre-bunking https://ift.tt/xDCKri9 May 24, 2026 at 10:58PM

Show HN: My homelab is outperforming the stock market https://ift.tt/ElOBCA2

Show HN: My homelab is outperforming the stock market https://stocks.sjer.red May 25, 2026 at 01:54AM

Show HN: Replacing a 3.4MB video with 40kb of GSAP https://ift.tt/ZWEPzHj

Show HN: Replacing a 3.4MB video with 40kb of GSAP https://ift.tt/Okea0bn May 25, 2026 at 12:59AM

Show HN: Running BitNet b1.58 inside DRAM by breaking DDR4 timing rules https://ift.tt/OLIGyHr

Show HN: Running BitNet b1.58 inside DRAM by breaking DDR4 timing rules I have been working on running BitNet b1.58 inside DRAM by intentionally breaking DDR4 timing rules. Also made a visual explainer: https://pcdeni.github.io/CaSA/explainer/ This is tested and works inside commercial off the shelf memory with custom memory controller in the FPGA. The underlying effect is well characterized in academic papers (cmu safari, simra, dram bender, etc). In the process of getting this to work I also made previously undocumented discovery about DDR behaviour: https://pcdeni.github.io/CaSA/explainer/xor-spread.html Overall it is a bit slow, since data (in full rows) needs to be moved even when what is actually needed is only the count of the '1' bits (popcount). To make it competitive memory die changes would be needed, but not as drastic as merging compute and memory into one silicon. This would then avoid the memory wall issue the industry is currently facing. May 23, 2026 at 10:54PM