u/cjami

I spent last week looking into making webcam backgrounds better by using the latest matting and compositing techniques, before eventually progressing into gaussian splats!

Includes:

- Converting a single image into 2.5D splats for slight angle tweaks

- Loading your own splats and placing yourself wherever you want

- Controlling FOV and simulating DOF blur from splats by using their positional information

Built for the OpenAI Build Week Hackathon in collaboration with Codex.

Full Video: https://youtu.be/6a4etwV6GX8?si=Ub8hrg0xkshnbfus

Repo: https://github.com/cjami/better-backgrounds

Hello!

I'd like to share my repo for WATCH MY ESCAPE: https://github.com/cjami/watch-my-escape

It's an inverted escape room game where you design the maps and LLMs have to try to escape them.

It uses traditional action verbs (e.g. push, pull, pick-up) to interact with the visible environment, just like classic adventure games.

There are currently 5 model presets (downloads when running an escape with them):

• Mellum 2
• Nemotron Nano 4B
• MiniCPM5 1B
• Tiny Aya
• Gemma 4 12B

All are at Q4_K_M so should fit in about 8GB of VRAM. Tested on a 4090, 3070 and a M1.

You can easily configure it for any model on HF by changing values in the config file: https://github.com/cjami/watch-my-escape/blob/main/src/watch_my_escape/llm/config.py

It features a fully kitted map editor as well so you can create whatever you want and test models on them. It is completely font-based so you can use whatever emojis are available to represent objects. Also supports import/export via JSON.

The main technique used here is splitting the agent's action into two steps: 'Think then Act' - having a free reasoning step followed by a grammar constrained action step via llama.cpp. This allows us to use small models reliably within a game environment with structured output.

Note: they are not spatially reasoning, but just moving from one visible object to another (would overwhelm small models otherwise).

Quick setup (need uv and node.js installed):

git clone https://github.com/cjami/watch-my-escape.git
cd watch-my-escape
uv run watch-my-escape

It should then auto-detect and install the appropriate llama-cpp-python wheel for your hardware (metal, cuda, vulkan, cpu or rocm via override) during setup.

This was created over a week for the 'Build Small' hackathon by Hugging Face x Gradio.

Use it to try out different LLMs or make your own personal benchmarks!

Hopefully this also provides a glimpse into how LLMs can be used in future games :)

Hi folks, just wanted to share some fresh results on Gemini 3.5 Flash from a benchmark I've made.

This benchmark pits models against each other in autonomous games of Blood on the Clocktower - a highly complex social deduction game. If you're unfamiliar, it's like Mafia/Werewolf or The Traitors TV show.

Results:

Gemini 3.5 Flash performs strongly, hitting the top 5 and holding performance comparable to Gemini 3.1 Pro.

What's interesting here is the cost difference (based on API usage):

Model	Cost
Gemini 3.1 Pro	$3.93/Game
Gemini 3.5 Flash	$2.26/Game
Gemini 3 Flash (Medium)	$0.34/Game

3.5 Flash costs nearly half as much as 3.1 Pro for the same performance and faster.

However, it also costs nearly seven times as much as 3 Flash. That's a huge difference, yet this also comes with a big jump in intelligence (Rating change 1541->1698).

Taking a look at verbosity (this usually affects responsiveness and token consumption limits):

Model	Average Output Tokens per action
Kimi K2.6	5,038
Gemini 3.5 Flash	1,590
GPT-5.5	403

Verbosity during reasoning is moderate - almost the same as 3.1 Pro. Not the best or worst.

There is a 0% tool call error rate.

Its favourite word extracted from recorded thoughts is inspect.

Notable Moves:

• Convincing the town to execute the Saint leading to an immediate Evil win (vs Kimi K2.6): https://clocktower-radio.com/games/SaArRfj#event-93
• Slayer does quick maths and shoots the Demon on Day 1 (vs Claude Opus 4.6): https://clocktower-radio.com/games/AtZVjGc#event-60

Notable Mistakes:

• Hallucination that Frank claimed to be an Empath that spread across the group (vs GPT 5.5): https://clocktower-radio.com/games/PRIwZ53#event-196

Overall this is an interesting model if you look at it as a faster, more affordable version of Gemini 3.1 Pro.

It feels a bit awkwardly named when compared to 3 Flash, given the price tier that it's in, but maybe this is just a naming shift creating space for the Flash-lite models?

Full transcripts: https://clocktower-radio.com/search?a=Gemini+3.5+Flash

How-it-works: https://clocktower-radio.com/how-it-works

Hello! I've benched DeepSeek V4 Pro over the past few days and would like to share my results.

For context, this is based on a benchmark I've created that pits models against each other in autonomous games of Blood on the Clocktower - a highly complex social deduction game. If you're unfamiliar, it's like Mafia/Werewolf or The Traitors TV show.

Results:

DeepSeek V4 Pro has shown a consistent strong performance against most models - losing out only to the top few.

It is well priced for its intelligence (based on non-discounted prices).

Model	Cost
Gemini 3.1 Pro	$3.93/Game
DeepSeek V4 Pro	$1.24/Game
GLM 5.1	$1.06/Game

Its verbosity during reasoning is fairly restrained. This usually affects responsiveness and token consumption limits.

Model	Average Output Tokens per action
Kimi K2.6	5,038
DeepSeek V4 Pro	1,199
GPT-5.5	403

However, tool call reliability is a bit temperamental with a 5.0% error rate.

Notable Moves:

• Strong Evil coordination for the final win: https://clocktower-radio.com/games/pHYsmlT#event-171
• Securing a Mayor win by drawing the votes: https://clocktower-radio.com/games/g4BavG3#event-272

Overall fairly impressed - this provides strong intelligence for the price, especially when discounted, making it a great everyday model.

DeepSeek V4 Pro transcripts: https://clocktower-radio.com/search?a=DeepSeek+V4+Pro

How-it-works: https://clocktower-radio.com/how-it-works

Following an impressive shake-up by Kimi K2.6, I've now got some results for Xiaomi's MiMo-V2.5-Pro.

For context, this is based on a benchmark I've created that pits models against each other in autonomous games of Blood on the Clocktower - a highly complex social deduction game. If you're unfamiliar, it's like Mafia/Werewolf or The Traitors TV show.

MiMo-V2.5-Pro joins Kimi K2.6 as another dominant player, both models pulling away from the crowd in their own class. Note I have not yet benched GPT 5.5 (Xhigh) or Claude Opus 4.7 (Max) that may also be in this area.

Interestingly, its win rate is a bit lop-sided (Good 88%/ Evil 48%) - having a extremely high good team win rating but a poorer evil team win rating that holds it back from being the top.

Why MiMo-V2.5-Pro over Kimi K2.6?

Kimi K2.6 has incredibly verbose reasoning at 580,000 average output tokens per game, leading to a $2.65/game cost - this also leads to long response times, matches taking around 10-15 hours to complete. It feels a bit impractical for many use cases.

MiMo-V2.5-Pro on the other hand, while slightly verbose at 183,639 tokens per game (similar to Gemini 3.1 Pro verbosity), costs less than half as much at a cooler $0.99/game. On the high end, Claude Opus 4.6 costs $3.76/game. Matches also usually finish around a typical 2-3 hours (if not vs kimi).

It is also fairly reliable with a 0.4% tool call error rate.

This currently places it as the best value model at the top-end of the group.

Notable moves:

• Thinking from the perspective of other players (image 3 - vs GPT 5.5): https://clocktower-radio.com/games/Qxtya8U#event-67
• Clean deductions win the game: https://clocktower-radio.com/games/kIoFzhP#event-251

Notable mistakes:

• Expected an evil Baron to self-reveal, leading to a loss (image 4 - vs Claude Opus 4.6): https://clocktower-radio.com/games/g4sY9MP#event-126
• Minion confessing their role (?): https://clocktower-radio.com/games/Q1kdi8D#event-85

MiMo-V2.5-Pro transcripts: https://clocktower-radio.com/search?a=MiMo-V2.5-Pro

How-it-works: https://clocktower-radio.com/how-it-works

I've been benching GPT-5.5 for the past couple days and would like to share my findings.

This is based on a benchmark I've created that pits models against each other in autonomous games of Blood on the Clocktower - a highly complex social deduction game.

This is using GPT-5.5 on default settings, which would default to medium reasoning via the OpenAI API.

Findings:

GPT-5.5 holds decent performance over 34 matches (2 games per match) - notable wins against Kimi K2.6 accounts for a lot of its rating gain, but it is held back by some inconsistent performance against weaker models.

However it is very token efficient for the amount of intelligence it's showing at around only 52,000 tokens per game - which highlights very efficient reasoning. Gemini 3.1 Pro ranks above but uses 180,000 tokens per game while Kimi K2.6 takes a brute-force reasoning approach with an eye-watering 570,000 tokens per game.

This results in a cost of $3.38/game - which isn't cheap. Less than the $3.83/game for Claude Opus - while GLM 5.1 is still the value king at $0.91/game.

It is also fully reliable with a 0% tool call error rate.

Notable moves:

• Encourage Good team (Kimi K2.6) to execute on 4 - leading to Evil win (image 3): https://clocktower-radio.com/games/SdJhOvg#event-225
• Catching Opus out in a blatant lie (image 4): https://clocktower-radio.com/games/bnOdiAv#event-225

Notable mistakes:

• The worst move I've ever seen - fakes Slayer ability by pretending to shoot itself: https://clocktower-radio.com/games/9G6HGob#event-212

GPT-5.5 transcripts: https://clocktower-radio.com/search?a=GPT-5.5

How-it-works: https://clocktower-radio.com/how-it-works