搜索“RAG”

leep. Same workload, far less scheduler pressure. For streaming ASR, that predictability matters because delayed partial transcripts can make a voice system feel slow even when average latency looks fine. Under load for streaming workflows, p50 and p90 latency looked healthy, but p95 would periodically spike by about 200 ms. The logs showed small queue depth and normal GPU times, but CPU functions that normally ran in under 5 ms suddenly took over 100 ms. Something in the background was stealing

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ommit to a file. You stay in the loop, but the loop gets much tighter. Example prompts: I need to reprioritize these 30 Linear tickets. Make me an HTML file with each ticket as a draggable card across Now / Next / Later / Cut columns. Pre-sort them by your best guess. Add a "copy as Markdown" button that exports the final ordering with a one-line rationale per bucket; Here's our feature flag config. Build a form-based editor for it, group flags by area, show dependencies between them, warn me if

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compute attention scores. (The convolutional mixing is only applied to Q and K, not V, because Q and K determine the attention scores, while V represents the content that gets averaged via these scores). Next to the sequence mixing shown in Figure 14, there is also a channel mixing component. It’s in principle similar though, so I am omitting the illustration. CCA appears to be a Zyphra-introduced attention mechanism that predates the ZAYA1-8B technical report. The standalone CCA paper, Compres

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focuses on helping organizations automate the SDLC in ways that deliver demonstrable ROI. Mayur Nagarsheth Mayur Nagarsheth is Head of Solutions Architecture at Augment Code, leveraging over a decade of experience leading enterprise presales, AI solutions, and GTM strategy. He previously scaled MongoDB's North America West business from $6M to $300M+ ARR, while building and mentoring high-performing teams. As an advisor to startups including Portend AI, MatchbookAI, Bitwage, Avocado Systems, an

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ether’s Whisper V3 large endpoint that provides high quality multi-lingual transcription at an optimized speed. Then a large language model translates that transcript. Here we leverage the latest advances of Deepseek V4 Pro as default translator. We also enable the user's input of a predefined list of translation rules to maintain the faithfulness and accuracy. Finally, the TTS model generates translated speech, allowing users to specify their desired voice characteristics in plain text. Togethe

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on process we use to model inference applications. Notice not only the seasonal variation (daily cycles) but also the long-term trend of increasing variability in demand as the average demand increases. Spiky demand raises serious engineering problems. To borrow from Marc Brooker of AWS: “the cost of a system scales with its (short-term) peak traffic, but for most applications the value the system generates scales with the (long-term) average traffic.” Spiky demand means high peak-to-average rat

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What the open-source library gives you; The architecture; API entry; The queue layer; The worker Lambda; State in S3; When Lambda runs out of time; Failure handling; Ephemeral storage cleanup; Why this works; What survived We open-sourced browser-use so anyone can run a browser agent locally with a few lines of Python. Running millions of those agents in production with retries, timeouts, screenshots, audit trails, and billing requires infrastructure that took us 4,000+ commits to get right. Th

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nce completes, the outputs includes a URL to the hosted video. We can download it using cURL and our Together API key: ‍ Note: -L is required to follow the http redirect in the storage url and -O will write the output to a local file. This story only works because Together's Dedicated Container Inference (DCI) is genuinely a great place to run models like this, and it's worth explaining why. DCI gives you a private, GPU-backed environment running the model of your choice, fully managed by Togeth

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earch, which focuses on improving what models know or can do. As inference costs become the dominant expense in AI deployment, inference research has become one of the highest-leverage areas in applied AI. How does inference optimization affect AI product economics? Inference optimization directly improves unit economics: faster inference means more requests served per GPU-hour, which translates to lower cost per request. At scale, even modest efficiency gains compound significantly — a 2x impro

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rk from Databricks, we're seeing results approaching frontier model performance for 1/30th of the cost. Closed-source wins on accuracy ( 80% vs 70%), but MiniMax M2.5 ran at an average of $1.74 per run versus $56.53 for Opus 4.5. That's roughly 30× cheaper. Switching to the @goose oss harness beat alternatives by 10% in accuracy and was 8× cheaper than the next option. We decided to further investigate this with independent testing. On Terminal Bench 2.0, we found a similar story—goose was 20x m

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within a project); Session context (cached within a session); Conversation messages This way we maximize how many sessions share cache hits. But this approach can be surprisingly fragile. We’ve broken this ordering before for a variety of reasons, including: putting an in-depth timestamp in the static system prompt, shuffling tool order definitions non-deterministically, and updating parameters of tools (e.g., what agents the Agent tool can call). USE MESSAGES FOR UPDATES There may be times when

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ne of the existing geostationary satellites were designed to map CO 2 . Google researchers used ERA to develop a single-pixel, physics-guided neural network to distill a column-averaged CO 2 signal from the existing GOES East observations. To do so, the model combines data from 16 wavelength bands from GOES-East with lower-troposphere meteorology, solar angles, and day of the year. After training on the sparse observations from OCO-2 and OCO-3 , the model was then able to derive estimates of col

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date its status without fully exiting back to the orchestrator. These subagent fields are made visible to the orchestrator via a list subagents tool: It took a great deal of "encouragement" to keep the orchestrator from exiting before its async tasks finished. Future work, as an exercise for the reader, could included implementing a special "self thought" tool or subagent to give the orchestrator a productive outlet for thinking/planning rather than waiting for subagent results or exiting early.

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with Code Intelligence and custom MCP servers, to investigate. This saves him from the tedium of looking up the right customer ID, logs, and metadata, and correlating information fragmented across various tools. Matthijs gets the best results from coding agents by breaking the work into small, targeted steps, which keeps the agent on track and creates a much narrower path to success. Stage 3: Close the loop with a happy customer⁠ Once Matthijs has resolved the bug, he marks the issue as ‘Done’ i

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should go back into the model on the next turn? So the emphasis is compression, clipping, deduplication, and recency. Now, this section, structured session memory, is about the storage-time structure of history. The question here is: what does the agent keep over time as a permanent record? So the emphasis is that the runtime keeps a fuller transcript as a durable state, alongside a lighter memory layer that is smaller and gets modified and compacted rather than just appended to. To summarize, a

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llation pipelines are expensive. Activation collection and replay pipelines for drafter training can be extremely costly to store and operate at scale. At production scale, the storage footprint can reach petabyte-level magnitude, with high cost in memory, bandwidth, and operational complexity. Aurora reduces this burden by learning directly from live serving traces. Acceptance rate is not the same as real speedup. Offline training can optimize acceptance in a lab setting, but production speedup

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compared to the solo run, where the central feature of the application simply didn't work, the lift was obvious. Iterating on the harness The first set of harness results was encouraging, but it was also bulky, slow, and expensive. The logical next step was to find ways to simplify the harness without degrading its performance. This was partly common sense and partly a function of a more general principle: every component in a harness encodes an assumption about what the model can't do on its ow

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set the stage for related attention concepts like grouped-query attention, sliding window attention, and so on. If you are interested in a longer, more detailed self-attention coverage, you might like my longer Understanding and Coding Self-Attention, Multi-Head Attention, Causal-Attention, and Cross-Attention in LLMs article. EXAMPLE ARCHITECTURES 1.2 Historical Tidbits And Why Attention Was Invented Attention predates transformers and MHA. Its immediate background is encoder-decoder RNNs for t

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bugging painful bugs. We built an autonomous agent that closes that gap. It reads Rails source files, generates or improves RSpec tests, validates them against style rules and coverage targets, and runs inside a CI/CD pipeline with no human intervention. To operate on codebases at this scale, it runs in parallel: multiple instances working on different files simultaneously. RSpec through an agent's eyes Ruby is dynamically typed: there is no compilation step, so errors surface at runtime. For ou

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ristic of the repetitive stuff is that often there's a fixed procedure you need to follow. For example, your org may have a particular way of doing reviews, or particular test coverage metrics that you need to achieve, or certain CVE scanners that need to be happy. When automating things with agents, we'd also like to have them follow these procedures. In this post, we'll introduce a way to get agents to follow these procedures: agent skills. Skills are specialised knowledge packages that extend

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