architecture
architecture at Beyond Market Intelligence is a file of 110 stories. The newest of them: “Build Your Own LLM Runtime with a Guided Tour of Real Bugs”, “How DoorDash Built a Proxy Cache Handling 1.5 Million Requests per Second”, and “Three InfoQ Certification Cohorts Launch This August with Expert Facilitators”. There's a particular satisfaction in packing your own weights and capturing your own CUDA graphs, and that's exactly the terrain this walkthrough covers. DoorDash's Entity Cache is a masterclass in handling scale without drama. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every architecture story on Beyond Market Intelligence, newest first.
Build Your Own LLM Runtime with a Guided Tour of Real Bugs
There's a particular satisfaction in packing your own weights and capturing your own CUDA graphs, and that's exactly the terrain this walkthrough covers. The post traces the building of annotated-llm-runtime on an H100, with three bugs generating most of the hard-won annotations. It's a practical, grounded tour for anyone curious about the mechanics beneath the abstraction. For broader context on the distributed side, *Unlock LLM Training* pairs well here, though this piece stands firmly on its own.
How DoorDash Built a Proxy Cache Handling 1.5 Million Requests per Second
DoorDash's Entity Cache is a masterclass in handling scale without drama. Serving over 1.5 million requests per second with 99.99999% availability, the platform uses Envoy and Valkey to cut redundant service-to-service calls across its microservices. That's not just impressive; it's the kind of quiet reliability that keeps complex systems humming. By leaning on event-driven invalidation and smart failure handling, DoorDash shows how transparent caching can transform performance. For teams wrestling with similar growth, this approach offers a practical, forward-thinking path worth exploring.
Three InfoQ Certification Cohorts Launch This August with Expert Facilitators
Three certification cohorts open this August, each led by a senior practitioner applying QCon talk frameworks directly to your work. Luca Mezzalira tackles architecture, Michelle Brush focuses on engineering leadership, and Katharine Jarmul addresses AI security and privacy. That is a solid lineup for five weeks of focused, practical learning. If you want to see how these frameworks scale beyond the classroom, our piece on scaling AWS deployments with Stateless Model Context Protocol offers a useful technical companion.
Explore how deep learning unlocks new insights in single-cell analysis.
A survey covering 25 deep learning methods across six subcategories for single-cell RNA sequencing analysis is a lot of ground to cover. The author of this paper didn't just list them; they organized each method by purpose, architecture, metrics, and, crucially, its specific novelty. That last part is what makes the survey genuinely useful for anyone trying to navigate the field. It's one thing to know a model works, another to understand why it was built differently.
Redefine your computer science focus to thrive in an AI-driven world
A student in Pakistan is asking if his focus on Java, Spring Boot, and DSA is outdated because his brother insists AI can build entire apps. He's right to question the hype, but wrong to abandon fundamentals. AI generates code; it doesn't make architectural decisions. Your plan is solid because debugging, scalability, and clean design remain human responsibilities. Don't chase every automation trend. Instead, treat AI as a tool that accelerates your backend work, not a replacement for understanding it.
Beyond language: How JEPA aims to close the gap between answering and doing
LeCun's point lands well: an LLM can describe the physical world without truly modeling it. That gap between explaining and doing is the real challenge. His push for JEPA as a path forward is compelling, but it raises a fair question. Are we chasing an architectural fix, or just hoping for a magic bullet that isn't in our toolbox yet? It's a worthwhile tension to explore. For more on how AI handles understanding, check out "Verify Your AI's Understanding: A Simple Check for Tax Season."
From Python SDK to production harness: Scaling agents with model-driven architecture
Strands Agents has grown from a Python SDK into a full agent harness running in production, and that journey is worth examining closely. Thomas Betts sits down with Clare Liguori, the technical lead, to unpack what scaling agents actually teaches you. Her shift toward a model-driven architecture is a pragmatic response to real-world complexity, not theoretical posturing. As LLMs improve, the questions shift from "can we" to "how reliably." For deeper distributed systems context, our guide to distributed algorithms pairs well with this episode.
Discover how AI can turn simple ASCII into powerful visual thinking.
Most benchmarks chase coding and math, but ASCIITermDraw-Bench asks something simpler and harder: can a model actually draw? We like this direction. It tests whether an AI can arrange boxes, labels, and arrows with precision, not just describe them. With 80 tasks across layouts, network topologies, and architecture diagrams, it measures a skill that's easy to overlook. The leaderboard shows real gaps, with Gemma-4-31B-IT leading at 73.8%. If you're exploring how AI handles visual reasoning through plain text, this is worth your attention.
Build Smarter Data Systems with AI Agents, Governance, and Practical Architecture
Most teams treat AI as an add-on, layering it over spreadsheets that were never built for it. A native architecture where data agents handle the heavy lifting, AI-powered QA keeps answers honest, and governance stays embedded from the start is argued for as something bolder. It's a practical blueprint, not theory. If you're questioning how much to trust these systems, our related piece on verifying AI's understanding offers a timely follow-up.
Explore smarter Python patterns and real-world projects to transform your workflow
If-else chains slow you down, and this week we're showing you a cleaner path. The Registry Pattern in Python offers a smarter, more maintainable approach to dispatching logic, and we're glad to spotlight it. Alongside that, you'll find practical SQL projects to strengthen your portfolio and YouTube channels that keep your AI knowledge sharp. For a deeper reflection on where this technology leads, check out "Talking to My AI Clone Taught Me to Question the Tech" for a thoughtful counterpoint on human interaction with AI.
Explore a new recurrent architecture built for deeper reasoning and memory tasks.
After months of independent work, DABSN (Dynamic Adaptive Bias State Network) is now public, with a preprint and code that includes PyTorch, C++, and Triton implementations. The architecture's behavior on reasoning and long-sequence benchmarks is compelling, but the 24M-parameter language model trained on 1B tokens is where the results get genuinely interesting. Now, the ask is for collaborators: independent reproduction, stronger evaluations, or GPU access to push scaling further. It's a practical, open invitation to build on something promising, not a promise of perfection.
When the Loop Has No Brain, the Architecture Speaks for Itself
Most discussions of loop engineering assume an LLM sits at the center of the loop. This experiment challenges that by removing the model entirely, using a deterministic, zero-dependency Python benchmark to isolate the architecture itself. The author tested whether a goal-directed controller could isolate failures better than a linear pipeline, validating it across 300 random seeds. The results are narrow but compelling: failure isolation is a measurable property of control flow, independent of reasoning.
Vint Cerf helps define a standard for identifying AI agents online
Vint Cerf, the mind behind TCP/IP, is now tackling a new frontier: giving AI agents a standard identity on the open internet. It's a pragmatic move from someone who knows that structure makes networks work. If agents are going to act on our behalf, we need to know who they are. It's a technical answer to a trust problem, and that's exactly the kind of foundation worth exploring.
Build a Context Store to Keep AI Development from Outpacing Understanding
Fast development is seductive. AI makes the first 80% of coding feel effortless, yet the structural debt hides until it surfaces as instability. That is why we find the argument for a "Context Store" so compelling. By binding spec-anchored design, tests, and fitness functions directly to the repo, Stella Berhe and her team show how we can make evolution safe. It is a practical shift from chasing speed to ensuring comprehension, and a smart next step for leaders.