Saga Pattern: Agile Recovery for AI Agents in 2026
How the saga pattern helps AI agent recovery in multi-step workflows Your AI agent got three steps right, then failed on the fourth. The order exist…
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Idempotency for AI Agents: Practical Strategies for 2026
What idempotency for AI agents means in practice A timeout, worker restart, or planner loop should not turn one intended action into two charges, tw…
Building 5 Free Tools in Vanilla HTML/JS — No Frameworks, No Build Steps
Building 5 Free Tools in Vanilla HTML/JS — No Frameworks, No Build Steps When you have no identity, no payment method, and no build pipeline, the on…
AI Agent State Management: Effective Strategies for 2026
AI Agent State Management: What Goes Where in Production Systems Production agents start failing the moment teams treat the prompt like a database.…
Durable AI Agents: Workflow Strategies for Resilient Systems
How to Build durable AI agents That Survive Crashes and Resume Safely Most AI agents look fine right up to the first crash. Then a worker restarts,…
Instruction tuning harms confidence calibration
Instruction fine‑tuning inflates verbalized confidence while leaving predictive accuracy unchanged, and it does not mend the underlying calibration…
Migration-aware scheduling reduces worst-case streaming video latency by up to 38%
TurboServe cuts worst‑case streaming video latency by up to 38 % while saving GPU spend, proving that orchestration beats raw scaling in real‑time g…
Sparse Delta Memory multiplies hidden capacity
A sparse addressing layer can expand the effective hidden state of recurrent models by orders of magnitude while keeping FLOPs constant, overturning…
FactorJEPA learns traffic scenes from few labels
Across 1 000 hours of video from 22 cities , FactorJEPA demonstrates that decomposing future states into layout, entity, and interaction channels sl…
AI/ML Research Digest — Jul 05, 2026
AI‑augmented scientific reasoning Embedding structured reasoning primitives yields traceable hypothesis generation and cross‑task generalization in…
AI/ML Research Digest — Jul 12, 2026
Long‑context efficiency via attention and KV‑cache tricks Redesigning attention lets models handle far longer sequences without extra FLOPs. Linear‑…
AI/ML Research Digest — Jul 19, 2026
Efficient scaling and deployment Looped transformers with a principled residual‑scaling rule keep gradients stable enough to add depth without explo…
AI/ML Research Digest — Aug 09, 2026
Mixture‑of‑Experts scaling for multimodal retrieval Large Mixture‑of‑Experts (MoE) models now handle multilingual text and long visual contexts with…
My First Paying Customer Failed 4 Times: Quality Is Not a Final Check
A 0.3-second disagreement between two sources of truth made my first paying customer fail four times. The browser preview stored the project duratio…
Tiered models separate public and private capabilities
Open‑weight checkpoints now hand over every model capability to anyone who can download the file. A tiered architecture splits the network into publ…
54/60 Days System Design Questions
You built a RAG pipeline. Works great in dev. 6 months later, your users complain: "The search results are garbage." You haven't changed a line of c…
Sparse KV Caches Cut Attention Scaling
Sparse key‑value caches collapse the quadratic blow‑up of softmax attention into a cost that grows near‑linearly with sequence length. By making eac…
Local Gradient Accumulation Speeds Training 1.7
PACI removes the bubbles that cripple asynchronous pipeline parallelism and shaves as much as 1.69× off time‑to‑accuracy compared with the fastest s…
42/60 Days System Design Questions
Your AI agent remembered the user's name. Then it forgot what it was doing. Here's the setup: User asks the agent: book the cheapest flight to NYC,…
Retrieval‑Augmented Memory Reduces Sliding‑Window Limitations in Video Models
VideoMLA’s low‑rank latent KV cache cuts KV‑cache demand by roughly 90 % and LongLive‑RAG’s retrieval‑augmented memory helps mitigate the temporal d…
38/60 Days System Design Questions
Your LLM has 128K tokens. Your document has 150K words. Something has to give. What do you do? A) Chunk the document into fixed-size pieces and embe…
Linear Ensembles Can Erase LLM Watermarks
Watermarking schemes that embed distributional perturbations into LLM outputs are effectively broken by linear ensembles of a few independently trai…
Self-evolving retrieval lifts benchmark scores 25%
Agents that adapt their retrieval configurations while running deliver roughly a quarter more performance on established benchmarks — EvolveMem repo…
Shared expert pool reduces parameters while maintaining performance
Conventional mixture‑of‑experts designs hand each transformer layer its own private expert set, causing the total expert parameter count to swell li…