Intercom 2X'd Engineering Velocity

Intercom claims 2x productivity increase with coding agents, and this podcast has some pretty interesting insights on on how they've been approaching this across their org: Many organisations are aggressively trying to figure out how to unlock AI coding productivity beyond the individual and across the organisation, which seems Intercom has figured out a way forward. In nine months, it seems they doubled merged PRs metric while keeping quality stable by treating the AI workflow like an internal product, instrumenting usage with telemetry, analyzing anonymized session data, and building a shared skills repository with hooks that enforce engineering standards automatically. It is great to see that often the secret is actually nothing more than solid engineering practices in the foundation; gains come less from "allow everyone to do tokenmaxing" and instead more from building the surrounding foundation for high quality PRs, flaky tests, CI, internal tools and reviews. This is the most important time to invest in CI/code-review bottlenecks, as well as DORA metric improvements, as well as a culture where PMs, designers and engineers can all safely ship code.

Qwen3.6-27B Coding

Chinese giant Alibaba releases another impressive open source model with Qwen3.6, with only 27B parameters which shows impressive performance on coding tasks: From the reports it seems that this release brings flagship-level agentic coding into a dense 27B open-weight model, which is super impressive how much can be packed in such a relatively small model. This basically reducess the complexity from very large MoE systems while still outperforming Qwen’s previous 397B-total / 17B-active open-source flagship on major coding-agent benchmarks like SWE-bench, etc The interesting bit is not just the scores, but the fact that this model is open sourced under Apache-2.0 and it supports text/image/video inputs, offers a 262K native context window extendable up to 1M tokens, and introduces "thinking preservation" for multi-turn agentic workflows. 

Scientific Theory for Deep Learning

It seems we're at a stage where deep learning is evolving from alchemy into an engineering discipline; this is an exciting paper which lays out that a scientific theory is emerging for Deep Learning: This is great as having a robust scientific foundation means fewer blind hyperparameter searches, more predictable scaling, better interpretability, and stronger foundations for safety. Deep learning theory is starting to look less like scattered math and more like an emerging "mechanics of learning" as a physics-style framework for predicting training dynamics, representations, final weights, and model performance. The paper breaks down into five buckets, including 1) solvable toy settings such as deep linear networks and NTKs; 2) useful limits such as infinite width/depth and lazy vs. rich feature learning; 3) empirical laws such as scaling laws and edge-of-stability behavior; 4) hyperparameter theories such as μP and learning-rate/batch-size scaling; and 5) universal phenomena where different architectures, datasets, and training recipes converge to similar representations. 

META KernelEvolve Optimizes AI Infrastructure

META is building agentic systems that are optimizing the AI infrastructure under their large scale machine learning models, and they have released their framework: META's KernelEvolve is a framework they are using for optimizing the low-level infrastructure that determines whether large-scale models are economically viable in production. KernelEvolve sits inside Meta’s Ranking Engineer Agent stack and turns kernel authoring into a closed-loop search problem across NVIDIA GPUs, AMD GPUs, MTIA chips, and CPUs, using LLM-generated candidates, retrieval-augmented hardware knowledge, tree search, profiling feedback, and automated correctness/performance evaluation. Meta reports compressing weeks of expert kernel work into hours, achieving over 60% inference throughput improvement for the Andromeda Ads model on NVIDIA GPUs and over 25% training throughput improvement for an ads model on MTIA, while supporting DSLs and backends like Triton, CuTe DSL, FlyDSL, CUDA, HIP, and MTIA C++. For production ML practitioners, the key takeaway is that the next bottleneck in model iteration may be less about model design alone and more about automating the systems layer around it: kernel generation, hardware portability, profiling, benchmarking, and continuous optimization across increasingly heterogeneous accelerator fleets.

OpenAI Releasing GPT-5.5

OpenAI has released their latest model GPT-5.5. It seems this model is positioned less as a chatbot and more as a long-running worker for coding, research, document-heavy knowledge work, tool use, and computer operation. For production ML practitioners the main callouts are that the new model is focused on stronger agentic coding and systems reasoning, better long-context performance up to 1M tokens, improved tool reliability, and greater token efficiency while maintaining GPT-5.4-like per-token latency. The benchmarks sound impressive (although we know how these are never confirmed until they are taken for a spin), with 82.7% on Terminal-Bench 2.0, 58.6% on SWE-Bench Pro, etc. Keen to see how this performs in the wild, would be great to hear experiences from practitioners as they take them into production projects.

The Institute for Ethical AI & Machine Learning is a European research centre that carries out world-class research into responsible machine learning.

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