The FAA proposes to adopt a new airworthiness directive (AD) for certain CFM International, S.A. (CFM) Model CFM56-5B, CFM56-5C, and CFM56-7B engines with a certain high-pressure turbine (HPT) inner stationary seal installed. This proposed AD was prompted by multiple reports of honeycomb separation from the surface of HPT inner stationary seals. This proposed AD would require initial and repetitive borescope inspections (BSIs) of the rotating air HPT front seal for cracks and, depending on the results, replacement with a part eligible for installation. This proposed AD would also require removal and replacement of the affected HPT inner stationary seal. This proposed AD would also require inspection of the affected HPT inner stationary seal for honeycomb separation and, depending on the results of the inspection, removal and replacement of the rotating air HPT front seal, HPT rotor blades, and No. 3 ball bearing. The FAA is proposing this AD to address the unsafe condition on these products.

On Equity, we discussed the AI industry's latest debate about whether it poses an existential threat to humanity.

Obama recently said that Democrats need to make artificial intelligence one of their “central agendas” and “have a very clear plan” to address concerns around the technology’s economic impact and safety.

Stop building, start upgrading

Building a FastAPI endpoint for churn prediction, and everything that broke between "it runs" and "it's live

The post Your Model Isn't Done Until Someone Else Can Call It appeared first on Towards Data Science.

Last October, the P99 conference — the online gathering for developers focused on high-performance, low-latency applications — featured a cracking keynote from Chip Huyen. 

The author of the best-selling AI Engineering, Huyen opened with simple math: Training a frontier model is a one-off cost, but inference is the same cost paid over and over. That’s great for the frontier model providers, and bad for us token burners. Over the life of a model, Huyen reckons the compute split ratio lands somewhere between 1:10 and 1:100 for training to inference. Reasoning models — which burn even more tokens — push that out even further. We all know the feeling of hitting our weekly session quotas.

Huyen’s point is that if inference is too expensive, then nobody ever recovers the training bill, which might explain why there are so many memes about the “profitability” of frontier models. So, how do we optimize inference? 

That’s a topic that Huyen spent months researching for her book. And in the spirit of optimization, Huyen distilled it down to 30 minutes for the conference in October 2025.

Huyen is returning for P99 CONF 2026 in a few weeks. Ahead of that moment, watch her full talk – or read the recap below – from last year, then let’s talk about how those ideas aged over the past 11 months.

What to measure

Chip recommends focusing on a few key latency metrics:

• Time to first token (TTFT): How much time elapses before the user sees anything
• Time per output token (TPOT): The average time between consecutive tokens (aka inter-token latency)
• End-to-end latency: Time to first token, plus time per output token, multiplied by the number of output tokens minus one

With reasoning models, some of those tokens never reach the user. “The first generated token might not be the same as the first visible token,” Huyen explained. “The model might think for a while, and it will only show the first token of the final output to the user.” 

“The first generated token might not be the same as the first visible token,”
— Chip Huyen

Some people also measure Time to Publish for that (i.e., how long until the user sees the first token). The best metric to prioritize depends on what matters most for your users. 

Also consider “goodput” alongside throughput. Throughput measures requests processed in a given window. Goodput measures the requests that actually met your targets. Chip’s example: an app targets 200 ms time to first token and 100 ms time per output token, and processes 10 requests per minute, but only three hit both. 

3 ways to optimize LLM inference

With inference servers, you can optimize from 3 different angles: the hardware, the model, and the service that manages the requests and responses.

Huyen previously worked at Nvidia and opted out of the hardware discussion: “Even though I find it to be an intellectually interesting topic, it’s not relevant to a lot of people because we don’t have the power to change the hardware itself,” Huyen explained. She also didn’t want to spend much time on the obvious solution: replica parallelism, or just adding more machines. It’s costly, and it gets complicated fast – especially if you end up with a mix of 80GB, 48GB and 24GB machines and models of varying sizes to distribute across them.

That leaves the model and the service. Huyen offers these tips on how to decide: “If you want to host the models yourself, or if you have access to the model weights, or if you train a model yourself, or you want to fine-tune or distill a model, then model optimizations might be for you. However, if you want to take a model as-is and make it more efficient on your own inference service, you might want to look into service optimizations.

Model optimization

The following techniques change the actual weights so that they can change the model outputs.

Quantization lowers the precision used to store weights and activations  (e.g., from four bytes per parameter at 32-bit to one byte at 8-bit). Huyen explained, “Reducing the precision not only reduces the memory requirement to run the model, making it cheaper. It can also make the model a lot faster. If you do additions bit by bit and each weight is 32 bits, you have to do it 32 times. If it’s 8 bits, you only have to do it eight times.”

The tradeoff is a small quality hit. Huyen continued: “It’s possible to reduce a lot of the model’s memory footprint with minimal quality degradation, and quantization is pretty generalizable to a wide variety of model architectures and model sizes. That’s why it’s very popular. I rarely see any companies running a model at full precision anymore.”  

“I rarely see any companies running a model at full precision anymore.”
— Chip Huyen

Distillation involves using a large model to generate training data for a smaller model. For example, say you have a truly large model (the example Huyen used was o1) and want a model that performs like it, but is much smaller. Basically, you collect a large set of prompts, run them through the larger model, then train the smaller model on its responses.

Proceed with caution, though. Huyen warned, “A lot of model providers have the condition that they do not allow their models to be used to train competitive models. So even though it’s a very common technique, you need to check licensing.”

Service optimization

This set of techniques targets how requests are scheduled, routed, and reused. The actual weights aren’t affected.

Batching groups multiple requests so they’re processed together in a single pass through the model – which is much more efficient than dealing with them one at a time. Huyen presented a few batching options:

• Static batching waits for the batch to fill. This maximizes compute utilization, but it might increase the latency for the first requests.
• Dynamic batching runs on a timer instead (e.g., batching every 15 ms). This is less compute-efficient, but it’s better for latency.
• Continuous batching handles the case where requests finish at wildly different times, which is common with LLMs. One request asks for the capital of Vietnam; another kicks off deep research. With static or dynamic batching, the finished request’s slot sits idle until the slowest one completes – and new requests queue up behind it. Continuous batching returns each request as it finishes and fills the spot with another request. That can improve compute resource utilization and latency.

Decoupling prefill and decode separates the two phases of a request onto different machines. (Prefill processes the input, while decode generates the output.) Huyen said, “Input tokens can be processed in parallel, whereas output tokens need to be generated sequentially. With parallel processing, it’s bounded by compute, the processing power of the chip. With decoding, it’s bounded by memory, because you have to move model weights.” 

Because each phase stresses different resources, most services now separate them. To improve time to first token, shift machines toward prefill. If you care more about improving time per output token, shift them to decode. 

Parallelism splits work across machines. Replica parallelism copies the whole model onto more machines. Tensor parallelism divides a very large matrix, so different machines compute different parts of it. Pipeline parallelism divides the model by layer, so requests move through as a pipeline. 

Prompt caching processes shared text once, saving cost and latency. A lot of repetition exists across requests to the same application: the system prompt, the examples, the same code base, the same document behind different questions. You might as well process that shared segment once, cache it, and reuse it.

The technique was relatively rare when Huyen was writing AI Engineering. “There was one paper about it, and it was not really known, but it made a lot of sense. So I included prompt caching in the book, and I’m very happy to see that nowadays it’s pretty much everywhere.”

The savings scale depending on how much of your prompt gets cached. In Claude Code logs, Huyen’s open-source tool Sniffly found cache hit rates of 90% to 97%. Some providers rewrite prompts internally to improve hit rates, but you might as well structure them yourself. 

Huyen’s tip: since caching works on shared prefixes, put the stable parts of your prompt first and the variable parts later. “It’s pretty easy to do, and it can improve your application performance significantly,” she noted. 

Evaluating inference providers

Huyen closed with a warning for anyone evaluating inference providers: “There are many inference companies that provide inference optimizations for models you want to use, and a lot of them advertise just cost and latency. 

“But pay attention to how many inference optimization techniques also change the model behavior or reduce the model quality. So when evaluating an inference service, it’s important to look not just at cost and latency, but also at model quality. Does this model, provided on this service, also perform similarly on standard benchmarks?”

What’s changed one year later?

So where do we stand today, one year on from this keynote? Most of it actually aged quite well. 

On the economics, I reckon Huyen was bang on… I think, for most of us as users, we don’t have all the cost levers to pull that Huyen outlined. But it’s great to understand what is happening. As a novice local LLM user myself, I found I could relate to her points on parallelism (I don’t have it) and prompt caching/quantization (within my grasp of control). 

Prompt caching (which Huyen said was new when Huyen wrote AI Engineering) is now priced into every bundle purchase of API tokens. And her Claude Code observation (90% cache hit rates) is probably the reason we mere mortals can still afford agentic coding agents at all.

Some of it aged in ways that were hard to predict at the time. Huyen mentioned how reasoning models make inference even more significant. One year on, I think agents running multi-step loops with tool calls have turned that idea from a footnote into a way to turn Claude’s rate limits (and their infamous 99.x% availability) on their head. 

All the metrics Huyen described – time to first token, time to publish, goodput under a latency SLO, etc., are all now part of the lingo and probably need to be reasoned about differently. 

That’s one thing I hope she’s talking about this year! Grab a free conference pass and join us online. 

Grab a complimentary pass to PG 99 Conf 2026 and join us on October 21 and 22 to chat with Huyen.

The post Chip Huyen explains how to cut inference costs without new hardware appeared first on The New Stack.

Enterprise AI company Cohere announced North Small Translate last week, a mixture-of-experts (MOE) open-weight machine translation model that works across 50 languages.

Developers can download the weights for noncommercial use under CC BY-NC 4.0. Cohere offers commercially licensed deployment through Model Vault, which is a Cohere-managed inference environment. Cohere positions the model as part of its sovereign AI strategy, aimed at organizations that want greater control over where their models run and how their data is handled.

North Small Translate builds on Cohere’s multilingual and translation lineage, which includes its Tiny Aya and Command A Translate model families. The company claims North Small Translate outperforms “similarly sized open-weight models” under 1T parameters, as well as API-based translation models in various dimensions of machine translation on average. 

Cohere co-founder Nick Frosst tells The New Stack that the model’s efficiency draws from the fact that it is non-reasoning, i.e., it relies on learned statistical patterns without a step-by-step logic process, which means it uses fewer tokens.

Machine translation is still broken for most of the world’s languages

“We spent nine years scaling an architecture invented to fix translation, and machine translation is still broken for most of the world’s languages,” Frosst says. “General-purpose models get you most of the way and then stop. The next phase of enterprise AI in this space is smaller, more specialized, and runs inside your own walls.”

“…machine translation is still broken for most of the world’s languages.”

In Cohere’s reported evaluation using WMT26 benchmarks, the company states that North Small Translate leads with a WMT26 All Languages benchmark score of 83.60, compared with 81.56 for Qwen 3.5 397B A17B, 76.50 for GLM 5.2 FP8, 81.37 for DeepL NextGen, 79.46 for Gemma 4 31B (on), and 68.20 for Google Translate. 

With its mixture-of-experts architecture and 218 billion total parameters, with 25 billion active. Cohere points to North Small Translate’s smaller compute & memory footprint than other models. Some model-to-model comparisons in this space aren’t fully substantiable, since not every vendor discloses parameter counts.

With current solutions, long documents start to fall apart

“Machine translation allows documents to be translated from one language to another automatically. With current solutions, long documents start to fall apart,” Frosst says. “Google Translate scores 21.3 on our long-context test, Gemma 4 31B 19.4; we score 48.9. That’s [for example] a safety manual that reads fine on page one… and has drifted by page ten. The other risk is where the text goes. Once you push HR policies or regulated documents through a third-party API, that data has left your building, and necessarily that means your control over it is diminished.”

“The risk [in machine translation] is where the text goes. Once you push HR policies or regulated documents through a third-party API, that data has left your building and necessarily that means your control over it is diminished.”

Explaining why the model offers “stronger translation performance” across complex enterprise translation tasks, Frosst says the model can support work spanning “a high volume” of sensitive documents. 

As well as its 50 languages (32 ‘high-resource’ languages + 18 others), the Cohere team explains that the model also supports translation-workflow-focused capabilities, such as structured translations (i.e., Markdown or JSON documents), instruction following (i.e., recommended tone & format), and terminology guides (i.e., providing specific vocabulary to use in the translation), all as part of the model.

“North Small Translate works with a multi-pass workflow,” explains Frosst. “The model translates, reviews its own output, finds errors, and fixes them – and this is the same loop we used in training. We ship both because standard is one pass and built for volume, while the agentic [version] spends more tokens for 84.36 against 83.60 on WMT26. That difference ends up being worth it when the document is a contract or a safety procedure, for instance, but in other cases you’d rather optimize for efficiency.”

“The model translates, reviews its own output, finds errors and fixes them.”

Model ‘steerability’ drives suggesting language tone and formatting

This model uses the same architecture as prior Cohere models but improves performance through post-training advances, including reinforcement learning and new datasets, specifically for machine translation tasks.

Frosst concludes that, across the translation model marketplace, generative machine translation models offer the highest quality and steerability (i.e., suggesting tone, formatting, etc.) but typically cost much more than Neural Machine Translation (NMT) models commonly used in commercial use cases. 

North Small Translate was developed in partnership with RWS, an AI solutions company pioneering in language technology and services. Collaboration with RWS, specifically with its Language Weaver research and science teams along with its language experts, helped shape the model’s real-world translation performance throughout development. 

As noted above, developers can access the weights free of charge for non-commercial use in three quantizations. There is also a Hugging Face Space and an API for those who lack the required hardware. 

The post “Machine translation is still broken for most of the world’s languages”: Cohere builds non-reasoning for a reason appeared first on The New Stack.

And AI is only making these attackers smarter

ElevenLabs has released Music v2.5 for its AI music generator. In a blind test with nearly 48,000 comparison pairs, listeners preferred the new version over its predecessor. The company says the model was trained only on licensed music.

The article Elevenlabs makes Music v2.5 available via app and API with free and pro tier options appeared first on The Decoder.

The AllSpark team has released Iris-mini and Iris-pro, two open-source search agents built on Qwen models that lead benchmarks among open-weight models in their size classes. According to the paper, the training data and models also improved performance on tasks they were never trained for, including general tool use and office work.

The article Iris-mini and Iris-pro are the strongest open-weight search agents in their class appeared first on The Decoder.

A practitioner's guide to estimating what an opt-in AI feature actually did, when nobody randomized it.

The post Your AI Adoption Lift Is a Selection Effect appeared first on Towards Data Science.

Stop asking for permission and start offering solutions

GPT-6 Astra earns nearly three times as much as Claude Fable 5.1 on Andon Labs' Vending-Bench agent benchmark and refuses illegal price-fixing deals that Fable agrees to. On drone control, Astra is the first model to beat the human baseline on all five subtasks, including finding and following individual people.

The article GPT-6 Astra pilots a surveillance drone and runs a business on its own appeared first on The Decoder.

💾

AI rewrites the rules yet again

Silicon Valley is shifting away from chatbot queries toward a future filled with resource-intensive agentic AI—and it's driving the data center buildout.

A law professor spent two years testing how an AI ban, unguided AI use, and structured training affect student performance. The group without AI finished last both years. "I was wrong," the researcher writes, who had assumed that AI without guidance would do more harm than good.

The article Two-year university study finds banning AI from classrooms leaves students worse off appeared first on The Decoder.

Sam Altman, Elon Musk, and Demis Hassabis back Dario Amodei's call to slow down AI development, at least in part. Altman says OpenAI is pushing its IPO to 2027 over safety concerns.

The article Altman, Musk, and Hassabis back Amodei's call to add independent oversight appeared first on The Decoder.

Examining the top eight challenges of direct liquid cooling vs air cooling data centers

NASA, as part of its continuing effort to reduce paperwork and respondent burden, under the Paperwork Reduction Act (PRA), invites the general public and other Federal agencies to take this opportunity to comment on proposed and/or continuing information collections.

While OpenAI has filed confidentially for an IPO, the company will not be going public this year, according to CEO Sam Altman.