Talks Worth Watching – Week of 2025-12-22
https://www.reddit.com/r/programming/comments/1ptm55f/talks_worth_watching_week_of_20251222/

<!-- SC_OFF -->This week's HFT Weekly roundup covers seven conference talks united by a common theme: domain knowledge beats generic optimization. Highlights include Greg Law's L3 logging library achieving 1ns latency by eliminating locks via memory-mapped buffers and atomic operations, Khalil Estell's 90% reduction in C++ exception overhead through smarter table search algorithms, Denis Yaroshevskiy explaining C++26 hazard pointers for lock-free config access at Meta, and Andrew Drakeford demonstrating a million-times speedup in leave-one-out regression by exploiting mathematical properties rather than just cache locality. The week's takeaway: before reaching for SIMD or optimizing data layout, spend time understanding what your code actually does at the domain level—that's where the biggest wins hide. <!-- SC_ON --> submitted by /u/OkSadMathematician (https://www.reddit.com/user/OkSadMathematician)
[link] (https://hftuniversity.com/2025/12/23/hft-weekly-2025-12-22-3/) [comments] (https://www.reddit.com/r/programming/comments/1ptm55f/talks_worth_watching_week_of_20251222/)

Posting my takeaways from AI Engineers Code (NYC) Conference
https://www.reddit.com/r/programming/comments/1ptnklp/posting_my_takeaways_from_ai_engineers_code_nyc/

<!-- SC_OFF -->Took a few days of watching to get through all the sessions of AIE Code but its finally done. Obviously a lot of marketing and self promotion in the talks but infinitely more value from the learnings and experiments from the companies working at the cutting edge of AI. Both in research and implementation. Hope y'all find this useful. <!-- SC_ON --> submitted by /u/ric03uec (https://www.reddit.com/user/ric03uec)
[link] (https://www.devashish.me/p/aie-code-2025-wrapup) [comments] (https://www.reddit.com/r/programming/comments/1ptnklp/posting_my_takeaways_from_ai_engineers_code_nyc/)

Evolution Pattern versus API Versioning
https://www.reddit.com/r/programming/comments/1ptrjnb/evolution_pattern_versus_api_versioning/

submitted by /u/apidemia (https://www.reddit.com/user/apidemia)
[link] (https://www.dotkernel.com/headless-platform/evolution-pattern-versus-api-versioning/) [comments] (https://www.reddit.com/r/programming/comments/1ptrjnb/evolution_pattern_versus_api_versioning/)

Test, don't (just) verify
https://www.reddit.com/r/programming/comments/1ptta7d/test_dont_just_verify/

submitted by /u/alpaylan (https://www.reddit.com/user/alpaylan)
[link] (https://alperenkeles.com/posts/test-dont-verify/) [comments] (https://www.reddit.com/r/programming/comments/1ptta7d/test_dont_just_verify/)

PyTorch vs TensorFlow in Enterprise Isn’t a Model Choice; It’s an Org Design Choice
https://www.reddit.com/r/programming/comments/1pttx2e/pytorch_vs_tensorflow_in_enterprise_isnt_a_model/

<!-- SC_OFF -->Most PyTorch vs TensorFlow debates stop at syntax or research popularity, but in enterprise environments the real differences show up later; deployment workflows, model governance, monitoring, and how easily teams can move from experiment to production. PyTorch often wins developer mindshare, while TensorFlow still shows up strong where long-term stability, tooling, and standardized pipelines matter. The “better” choice usually depends less on the model and more on how your org ships, scales, and maintains ML systems. This guide breaks down the trade-offs through an enterprise lens instead of a hype-driven one: PyTorch vs TensorFlow (https://www.netcomlearning.com/blog/pytorch-vs-tensorflow-enterprise-guide) What tipped the scale for your team; developer velocity, production tooling, or long-term maintainability? <!-- SC_ON --> submitted by /u/netcommah (https://www.reddit.com/user/netcommah)
[link] (https://www.netcomlearning.com/blog/pytorch-vs-tensorflow-enterprise-guide) [comments] (https://www.reddit.com/r/programming/comments/1pttx2e/pytorch_vs_tensorflow_in_enterprise_isnt_a_model/)

How to Make a Programming Language - Writing a simple Interpreter in Perk
https://www.reddit.com/r/programming/comments/1ptv10b/how_to_make_a_programming_language_writing_a/

submitted by /u/daedaluscommunity (https://www.reddit.com/user/daedaluscommunity)
[link] (https://www.youtube.com/watch?v=rNvCCU6zz2M) [comments] (https://www.reddit.com/r/programming/comments/1ptv10b/how_to_make_a_programming_language_writing_a/)

LLVM considering an AI tool policy, AI bot for fixing build system breakage proposed
https://www.reddit.com/r/programming/comments/1ptwvch/llvm_considering_an_ai_tool_policy_ai_bot_for/

submitted by /u/Fcking_Chuck (https://www.reddit.com/user/Fcking_Chuck)
[link] (https://www.phoronix.com/news/LLVM-AI-Tool-Policy-RFC) [comments] (https://www.reddit.com/r/programming/comments/1ptwvch/llvm_considering_an_ai_tool_policy_ai_bot_for/)

Agent Tech Lead + RTS game
https://www.reddit.com/r/programming/comments/1ptwzo7/agent_tech_lead_rts_game/

<!-- SC_OFF -->Wrote a blog post about using Cursor Cloud API to manage multiple agents in parallel — basically a kanban board where each task is a separate agent. Calling it "Agent Tech Lead". The main idea: software engineering is becoming an RTS game. Your company is the map, coding agents are your units, and your job is to place them, unblock them, and intervene when someone gets stuck. Job denoscription for this role if anyone wants to reuse: https://github.com/kyryl-opens-ml/ai-engineering/blob/main/blog-posts/agent-tech-lead/JobDenoscription.md <!-- SC_ON --> submitted by /u/Such_Tale_9830 (https://www.reddit.com/user/Such_Tale_9830)
[link] (https://kyrylai.com/2025/12/23/becoming-an-aiagent-tech-lead/) [comments] (https://www.reddit.com/r/programming/comments/1ptwzo7/agent_tech_lead_rts_game/)

OS virtual memory concepts from 1960s applied to AI: PagedAttention code walkthrough
https://www.reddit.com/r/programming/comments/1ptxiqe/os_virtual_memory_concepts_from_1960s_applied_to/

<!-- SC_OFF -->I came across vLLM and PagedAttention while trying to run LLM locally. It's a two-year-old paper, but it was very interesting to see how OS virtual memory concept from 1960s is applied to optimize GPU memory usage for AI. The post walks through vLLM's elegant implementation of block tables, doubly-linked LRU queues, and reference counting in optimizing GPU memory usage. <!-- SC_ON --> submitted by /u/noninertialframe96 (https://www.reddit.com/user/noninertialframe96)
[link] (https://codepointer.substack.com/p/vllm-pagedattention-saving-millions) [comments] (https://www.reddit.com/r/programming/comments/1ptxiqe/os_virtual_memory_concepts_from_1960s_applied_to/)

An interactive explanation of recursion with visualizations and exercises
https://www.reddit.com/r/programming/comments/1pty3uv/an_interactive_explanation_of_recursion_with/

<!-- SC_OFF -->Code simulations are in pseudocode. Exercises are in javanoscript (nodejs) with test cases listed. The visualizations work best on larger screens, otherwise they're truncated. <!-- SC_ON --> submitted by /u/dExcellentb (https://www.reddit.com/user/dExcellentb)
[link] (https://larrywu1.github.io/recursion) [comments] (https://www.reddit.com/r/programming/comments/1pty3uv/an_interactive_explanation_of_recursion_with/)

iceoryx2 v0.8 released
https://www.reddit.com/r/programming/comments/1ptyr3a/iceoryx2_v08_released/

submitted by /u/elfenpiff (https://www.reddit.com/user/elfenpiff)
[link] (https://ekxide.io/blog/iceoryx2-0.8-release/) [comments] (https://www.reddit.com/r/programming/comments/1ptyr3a/iceoryx2_v08_released/)

Fabrice Bellard Releases MicroQuickJS
https://www.reddit.com/r/programming/comments/1pu1ois/fabrice_bellard_releases_microquickjs/

submitted by /u/Ok-Tune-1346 (https://www.reddit.com/user/Ok-Tune-1346)
[link] (https://github.com/bellard/mquickjs/blob/main/README.md) [comments] (https://www.reddit.com/r/programming/comments/1pu1ois/fabrice_bellard_releases_microquickjs/)

How Monitoring Scales: XOR encoding in TSBDs
https://www.reddit.com/r/programming/comments/1pudwss/how_monitoring_scales_xor_encoding_in_tsbds/

submitted by /u/Helpful_Geologist430 (https://www.reddit.com/user/Helpful_Geologist430)
[link] (https://youtu.be/666Z9Imq2dE?si=mMy0wysg1ZYNeuvJ) [comments] (https://www.reddit.com/r/programming/comments/1pudwss/how_monitoring_scales_xor_encoding_in_tsbds/)

Why runtime environment variables don't really work for pure static websites
https://www.reddit.com/r/programming/comments/1puju4z/why_runtime_environment_variables_dont_really/

submitted by /u/Ok_Animator_1770 (https://www.reddit.com/user/Ok_Animator_1770)
[link] (https://nemanjamitic.com/blog/2025-12-21-static-website-runtime-environment-variables) [comments] (https://www.reddit.com/r/programming/comments/1puju4z/why_runtime_environment_variables_dont_really/)

We reduced transformer inference calls by ~75% without changing model weights (MFEE control-plane approach)
https://www.reddit.com/r/programming/comments/1puks9j/we_reduced_transformer_inference_calls_by_75/

<!-- SC_OFF -->I’ve been working on a systems paper proposing a simple idea: instead of optimizing how transformers run, decide whether they need to run at all. We introduce Meaning-First Execution (MFEE), a control-plane layer that gates transformer inference and routes requests into: - RENDER (run the model) - DIRECT (serve from cache / deterministic logic) - NO_OP (do nothing) - ABSTAIN (refuse safely) On a representative replay workload (1,000 mixed prompts), this reduced transformer execution by 75.1% while preserving 100% output equivalence when the model was invoked. Below is a derived economic impact table showing what that reduction implies at scale. These are not claims about any specific company, just linear extrapolations from the measured reduction. Economic Impact (Derived) Example Workload Savings (Based on Original Paper Results) Workload Type Daily Requests Transformer Reduction Annual GPU Cost Savings Web Search-like 8.5B 75% $2.1B – $4.2B Code Assist 100M 80% $292M – $584M Chat-style LLM 1.5B 70% $511M – $1.0B Enterprise API 10M 75% $27M – $55M Assumptions: - GPU cost: $1.50–$3.00/hr - Standard transformer inference costs - Linear scaling with avoided calls - Based on 75.1% measured reduction from the paper If you think these numbers are wrong, the evaluation harness is public. What surprising to me is that a lot of effort in the ecosystem goes toward squeezing marginal gains out of model execution, while the much larger question of when execution is even necessary seems to be the more important examination. MFEE isn’t meant to replace those optimizations. It sits upstream of them and reduces how often they’re even needed in the first place. Thoughts? <!-- SC_ON --> submitted by /u/anima-core (https://www.reddit.com/user/anima-core)
[link] (https://zenodo.org/records/18045379) [comments] (https://www.reddit.com/r/programming/comments/1puks9j/we_reduced_transformer_inference_calls_by_75/)

2025: The year SwiftUI died
https://www.reddit.com/r/programming/comments/1puky96/2025_the_year_swiftui_died/

submitted by /u/jacobs-tech-tavern (https://www.reddit.com/user/jacobs-tech-tavern)
[link] (https://blog.jacobstechtavern.com/p/the-year-swiftui-died) [comments] (https://www.reddit.com/r/programming/comments/1puky96/2025_the_year_swiftui_died/)

Serverless Panel • N. Coult, R. Kohler, D. Anderson, J. Agarwal, A. Laxmi & J. Dongre
https://www.reddit.com/r/programming/comments/1pun1w0/serverless_panel_n_coult_r_kohler_d_anderson_j/

submitted by /u/goto-con (https://www.reddit.com/user/goto-con)
[link] (https://youtu.be/eBOkg-GbCSE?list=PLEx5khR4g7PJy5LslEV6EbSEhmw-88ezL) [comments] (https://www.reddit.com/r/programming/comments/1pun1w0/serverless_panel_n_coult_r_kohler_d_anderson_j/)

GitHub repos aren’t documents — stop treating them like one
https://www.reddit.com/r/programming/comments/1pun9oq/github_repos_arent_documents_stop_treating_them/

<!-- SC_OFF -->Most repo-analysis tools still follow the same pattern:
embed every file, store vectors, and rely on retrieval later. That model makes sense for docs.
It breaks down for real codebases. Where structure, dependencies, and call flow matter more than isolated text similarity. What I found interesting in an OpenCV write-up is a different way to think about the problem:
don’t index the repo first, navigate it. The system starts with the repository structure, then uses an LLM to decide which files are worth opening for a given question. Code is parsed incrementally, only when needed, and the results are kept in state so follow-up questions build on earlier context instead of starting over. It’s closer to how experienced engineers explore unfamiliar code:
look at the layout, open a few likely files, follow the calls, ignore the rest. In that setup, embeddings aren’t the foundation anymore, they’re just an optimization. <!-- SC_ON --> submitted by /u/Different-Opinion973 (https://www.reddit.com/user/Different-Opinion973)
[link] (https://learnopencv.com/how-to-build-a-github-code-analyser-agent/) [comments] (https://www.reddit.com/r/programming/comments/1pun9oq/github_repos_arent_documents_stop_treating_them/)

Choosing the Right C++ Containers for Performance
https://www.reddit.com/r/programming/comments/1pup1re/choosing_the_right_c_containers_for_performance/

<!-- SC_OFF -->I wrote a short article on choosing C++ containers, focusing on memory layout and performance trade-offs in real systems. It discusses when vector, deque, and array make sense, and why node-based containers are often a poor fit for performance-sensitive code. <!-- SC_ON --> submitted by /u/Clean-Upstairs-8481 (https://www.reddit.com/user/Clean-Upstairs-8481)
[link] (https://techfortalk.co.uk/2025/12/24/optimal-c-containers-for-performance-efficiency/) [comments] (https://www.reddit.com/r/programming/comments/1pup1re/choosing_the_right_c_containers_for_performance/)

What This Year Taught Me About Engineering Leadership
https://www.reddit.com/r/programming/comments/1pupmxb/what_this_year_taught_me_about_engineering/

submitted by /u/gregorojstersek (https://www.reddit.com/user/gregorojstersek)
[link] (https://newsletter.eng-leadership.com/p/what-this-year-taught-me-about-engineering) [comments] (https://www.reddit.com/r/programming/comments/1pupmxb/what_this_year_taught_me_about_engineering/)

Ruby 4.0.0 Released | Ruby
https://www.reddit.com/r/programming/comments/1pvaq8y/ruby_400_released_ruby/

submitted by /u/LieNaive4921 (https://www.reddit.com/user/LieNaive4921)
[link] (https://www.ruby-lang.org/en/news/2025/12/25/ruby-4-0-0-released/) [comments] (https://www.reddit.com/r/programming/comments/1pvaq8y/ruby_400_released_ruby/)