Ponytail Skill for Claude Code: Does It Really Cut Tokens

An interesting read about measuring token savers. Shows how you can compare different tools if you have enough time 💀

https://blog.jetbrains.com/ai/2026/07/ponytail-skill-claude-tested/

#ai

How I made Unity handle half-a-million projectiles without dying : r/Unity3D

https://www.reddit.com/r/Unity3D/comments/1vn9aye/how_i_made_unity_handle_halfamillion_projectiles/

#optimization

Rendering at scale: Efficient strategies for massive object counts

Mega Cat Studios shared practical rendering lessons from Backyard Baseball: static batching, GPU instancing, vertex animation textures, culling, and the URP vs HDRP choice.

What I liked most is that the post starts where performance work should start: profile first, then optimize. They explicitly separate CPU- and GPU-bound problems, then explain the tradeoffs. VAT saves CPU time but adds memory pressure, batching saves draw overhead but can increase GPU memory.

This is not a magic checklist, of course. Still, real production notes from the tech trenches are much more useful than another “optimize your draw calls” summary.

https://unity.com/blog/rendering-at-scale-efficient-strategies-for-massive-object-counts

#performance #graphics #optimization

PerfLint puts AI optimization on measured ground

I have shared before the example how I used LLM and Tracy to profile a godot game.
And recently I experimented a lot with automating profiling our Unity game at work with the help of agents.

So I’m always skeptical when an AI agent “optimizes” a Unity project by reading files and guessing or even hallucinating (happened a few times in our team).
PerfLint takes a better route: deterministic rules measure assets, import settings, memory and build size, then expose findings through Unity CLI/MCP so an agent can explain or draft a fix.

The author reports Addressables duplication dropping from 337 assets to 5, and bundles shrinking from 1.29 GB to 805 MB. More interestingly, the tool caught an agent’s false texture diagnosis before it changed anything.

I haven’t tried it yet, take the numbers with a grain of salt, but “measure first, let AI explain” is the direction I want.

Have you tried it before or after this latest update?

https://discussions.unity.com/t/released-perflint-free-local-audit-for-build-size-performance-and-unity-6-migration/1734256

#performance #ai

I accidentally spent 22 months of my life because I thought text was simple : r/Unity3D

Has anyone here tried the free or paid version of UniText?
How was your experience?

https://www.reddit.com/r/Unity3D/comments/1vywmo7/i_accidentally_spent_22_months_of_my_life_because

Google Play makes mobile memory a release metric

Google Play is adding game quality thresholds from February 2027: anonymous RSS plus swap, bitmap memory, and DEX optimization. The key detail is the measurement model: 28-day P90 data by app state and device-RAM tier, not one dev-device capture. For games, foreground limits range from 2.25 GB on 4 GB devices to 5 GB on 16 GB; games over 50 MB of DEX face 25% minimums for shrinking, optimization, and obfuscation.

New metrics are already available in Google Play Vitals.

https://developer.android.com/blog/posts/elevating-app-quality-reducing-memory-usage-and-improving-device-migration

#performance #memory #android

Memory Profiler finds the roots behind Unity leaks

I keep coming back to this question with memory tools: not only "where was this allocated?" but "what is keeping it alive?"

Memory Profiler 1.2.0-pre.1 adds a Roots & Impact table attributing snapshot memory to static fields, scenes, AssetBundles, native subsystems, and other roots. It lets you sort leaked objects by total impact, including native and graphics memory they keep alive. It also captures CoreCLR Player snapshots, tho the VM’s own native allocations remain Untracked.

https://discussions.unity.com/t/memory-profiler-1-2-0-pre-1-released-roots-impact-profiling-coreclr-support/1735523

#memory #profiling #unity

Unity Pipeline 0.6 adds hot reload for IL2CPP

It is experimental, with a limited [HotReload] C# subset.

In the Editor, open the Window > Pipeline > Settings window, then in the Watcher section, click on Start. Note that you need to put the [HotReload] attribute on methods you intend to edit ahead of time and that it does not support all possible code changes - adding fields to a class is not supported etc.
source

https://discussions.unity.com/t/unity-pipeline-package-0-6-0-exp-1-is-available-now/1735626

#cli

Have you tested Astra already?

I checked deepswe bench and decided to test it with medium effort as it has shown the same result as sol max but cheaper.

I implemented 2 mid-size tasks and spent ~30% of a weekly limit on pro x5. Which subjectively looks more expensive than sol at higher efforts.

At least they gave us 2 more weekly resets.

Quality is the same as I had no major issues with sol before too.

What about your experience?

Unity Plugin

Official Unity plugin for Claude.

https://claude.com/plugins/unity

#ai

Netcode for GameObjects 3.0.0 is now available

It is preparation for Unity’s shared Netcode stack: Netcode for Entities 7.0 becomes a hard dependency, Unity 6.7 is the minimum supported Editor

The interesting caveat: NGO does not actively use Entities yet. So the dependency and migration surface arrive before the feature payoff.

https://discussions.unity.com/t/netcode-for-gameobjects-3-0-0-is-now-available/1736792

#unity #netcode

Claude Code effort level and model selection

effort means more than just "thinking time." Effort level controls how much work Claude does on your request overall. This does include how long the model thinks, but also:

How many files it reads;
How much it verifies; and
How far it pushes through a multi-step task before checking in with you.
At a higher effort, Claude will take more of those actions (for example, read files, run tests, and double-check) before it comes back to you. At lower effort, it would rather ask you for more context than spend tokens figuring something out on its own

They recommend starting with the default effort, as it fits most users and their tasks in terms of efficiency and token usage. Then if anything goes wrong based on the result switch the model, effort, or add more context.

The model setting is roughly how capable; the effort setting is roughly how thorough. Most real tasks need some of both.

https://claude.com/blog/claude-model-and-effort-level-in-claude-code

#llm

Introducing Claude Opus 5.5 \ Anthropic

Cost and speed. Opus 5.5 requires less compute to serve than Opus 5, and its pricing reflects that. Our tests show that at default settings it will cost 40% less than Opus 5 on typical workloads. Input and output tokens are $4 and $20 per million, 20% less than Opus 5. Cache reads (which make up the majority of agentic and coding work costs) are $0.20 per million tokens, 60% less than Opus 5. Opus 5.5 also generates output more than 30% faster than Opus 5.

In addition to the price drop, we’re increasing five-hour usage limits on Pro, Max, Team, and seat-based Enterprise plans. We’re also providing subscription users a rate limit reset, which you can now save and use whenever you choose.

https://www.anthropic.com/claude-opus-5-5

#ai

Introducing GPT‑6 Sol and Luna

And better & cheaper sol and luna as an immediate response to the opus release.

Time for a lot of testing this night 💀

https://openai.com/index/introducing-gpt-6-sol-and-luna/

#ai