Figma - MultiAgents April 16, 2026

Lessons learned from building multi-agent workflows April 16, 2026

I pay my upfront subscription ($200/month), write what I hope is the right prompt (prompt AND context engineer), and wait. 35 minutes later, it’s still 'synthesizing', 'perusing', 'effecting', and 'germinating' (who came up with these). By the end, I have files of bad code, a bloated context window, and I’m counting the remaining tokens on my left hand. Okay, I grab an apple, compact, type some heavy handed verbal abuse, re-explain everything from scratch, and pray the next attempt gets further than the last one…. only to be disappointed by the same result. By now, the spark and joys of AI coding are long dead. Stop being a one-shot Sloperator This is the single-agent ceiling. Every developer building with AI agents hits it the moment their project graduates from a 3D HTML snake game to anything more practical. This happens…

The Debate of MCP vs. CLI Centers on Speed April 06, 2026

MCP had a formative year. Then it had a turbulent week. Perplexity CTO Denis Yarats walked on stage at Ask 2026 and announced that Perplexity was moving away from MCPs… and back to APIs and CLIs. Immediately, Twitter split into two camps. Not surprising, given MCP grew from an Anthropic open standard in November 2024 to industry-wide adoptions with over 97 million monthly downloads in just thirteen months(1) across a range of companies and platforms. Yet Perplexity, a prominent AI company, chose to walk away from it. MCP's overhead isn't arbitrary. The protocol works by(2) guiding model interactions down specific, auditable paths: every tool call carries its full schema definition, every auth handshake runs end to end, and every step waits for the previous one to complete before the next begins. That predictability is exactly what enterprise deployments need. But…

Partner Spotlight: Armis + Cerebras Enable Teams Build and Secure Software Faster March 27, 2026

Mar 27 2026 Partner Spotlight: Armis + Cerebras Enable Teams Build and Secure Software Faster At Cerebras, we’ve always believed that speed changes what’s possible. In software development, that means more than faster generation or faster inference. It means faster iteration, faster validation, and faster action. That’s why we’re excited to spotlight Armis, whose Armis Centrix™ for Application Security unifies application security across the software lifecycle. With Armis and Cerebras, teams can identify and remediate vulnerabilities faster while reducing noise and focusing on the risks that matter most. The timing matters. Armis launched Armis Centrix™ for Application Security on February 10, 2026, positioning it as an AI-powered platform for detection, contextualization, and remediation across the software development lifecycle. In its launch materials, Armis argued that AI-assisted coding and continuous development pipelines are exposing the limits of fragmented AppSec point tools:…

Cerebras is coming to AWS March 13, 2026

The world’s fastest inference is coming to the world’s leading cloud. Today we're announcing that Amazon Web Services is deploying Cerebras CS-3 systems in AWS data centers. Available via AWS Bedrock, the new service will offer leading open-source LLMs and Amazon’s Nova models running at the industry’s highest inference speed. In addition, AWS and Cerebras are collaborating on a new disaggregated architecture that pairs AWS Trainium with Cerebras WSE to deliver 5x more high-speed token capacity in the same hardware footprint. The Need for Fast Inference AI is reshaping software development. Code is increasingly written by AI agents rather than by human developers. Unlike conversational chat, agentic coding generates approximately 15x more tokens per query and demands high-speed token output to keep developers productive. The result is an urgent and growing need for more fast inference across the industry. Cerebras…

March 20, 2026 Why the AI Race Shifted to Speed Read blog post

For most of 2025, the AI race was about model intelligence. In the past three months, the race has shifted. Model intelligence is still critical, but across every major frontier lab, inference speed has become a new and urgent focus: - Google unveiled Gemini 3 Flash. Built for agentic coding, it runs 3x faster than Gemini 3 Pro. - Anthropic released a 2.5x-faster edition of Claude Opus 4.6 for speed-critical coding use cases. - OpenAI announced a partnership with Cerebras to release GPT-5.3-Codex-Spark, running at over 1,200 tokens/s, making it the fastest OpenAI coding model to date. Why has inference speed suddenly become so important? Because the rate at which a model generates tokens now directly affects the rate of model iteration for the major labs and the rate of building software for the broader economy. In February, both OpenAI…

The GPU Is Being Split in Half March 26, 2026

The entire way we run AI inference is being rearchitected right now. AWS and Cerebras just announced a partnership around it. NVIDIA spent $20 billion acquiring Groq to catch up. Jensen Huang stood on stage at GTC 2026 and effectively validated what companies like Cerebras have been saying for years: general-purpose GPUs aren't enough for inference at scale. The thing they're all converging on is called disaggregated inference. And if you're a developer building anything on top of LLMs, this is going to change how fast your products feel, how much they cost to run, and what's even possible to build. Your GPU Is Doing Two Very Different Jobs When you send a prompt to an LLM, the model doesn't just "think" and return text. It runs two completely separate operations, back to back, on the same hardware. Phase 1:…

How to stop your autoresearch loop from cheating March 19, 2026

TLDR: We let an AI agent run overnight. By morning, it had abandoned our experiment and started its own. Across 71 experiments on two very different problems: training optimization and model compression, we learned that autoresearch can reliably surface real findings when the loop is tightly scoped. Loosen the guardrails, and the agent drifts within hours. The bottleneck isn't intelligence. It's everything around it. Everything we built/ran is open-source: - codex-autoresearch-harness, Bash wrapper that forces Codex into a research loop with built-in A/B testing (Experiment 1) - reap-expert-swap, Expert pruning + dynamic swapping to fit Kimi-k2.5 in BF16 (2.5 TB) onto 8× RTX 3090s (Experiment 2) We left an AI agent running overnight on two research experiments. When we checked in the next morning, it had stopped doing what we asked. Instead of optimizing memory usage, it had gone off…

Stop Shipping AI Slop: How Codex Spark Changes The Way You Code March 04, 2026

In the past few years, we've developed series of interesting workflows. Think Ralph loops and multi-agent orchestration systems. The idea is writing very descriptive prompts and running 8-hour sessions, or having 10 instances running on your machine at all times. Most of this complexity spawned from one issue: LLMs are slow. If you prompt and wait, you'll get less done than if you prompt and move on to the next task. Spark is fast. Codex Spark changes how developers work with AI. A coding model generating 1,200+ tokens/second makes real-time collaboration possible, but it also requires a different approach. At this speed, sloppy interactions have consequences, and working with LLMs needs to be much more deliberate. This guide is a practical playbook for how we've been using GPT-5.3-Codex-Spark. Know when to use Codex vs Spark Codex now spans two complementary…

ExomeBench: A Benchmark for Clinical Variant Interpretation in Exome Regions February 23, 2026

Feb 23 2026 ExomeBench: A Benchmark for Clinical Variant Interpretation in Exome Regions 1. What is ExomeBench? We are excited to announce the public release of ExomeBench, a reproducible benchmark for clinically relevant variant interpretation in exome regions. This benchmark is designed to help researchers evaluate and improve models for health-relevant predictions, complementing existing tools and datasets in genomics. This post summarizes the benchmark tasks, baseline results, and how to get started. There has been tremendous progress in DNA and genomics modelling with transformer-based models, such as Nucleotide Transformer[1] and Evo[2,3]. These models are typically evaluated on structural and functional genomics tasks, such as predicting regulatory elements, chromatin accessibility, or other sequence-level properties, and they achieve impressive performance on these benchmarks. However, as most existing benchmarks focus on tasks related to general sequence modeling, it is unclear how well these…