# deepseek AI developer platform offering model APIs for building applications. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## DeepSeek's new model sets a template for powerful LLMs that run lean DevFeed: [DeepSeek's new model sets a template for powerful LLMs that run lean]() Original publisher: [Read original article]() Author: Tobias Mann Published: 2026-09-11T07:15:00Z Content type: news Language: en Sources: [www.theregister.com - Articles]() Topics: [deepseek](), [Large Language Model](), [Caching](), [Latency](), [Google]() Tags: [ai-and-ml](), [cache](), [compute](), [cost](), [datacenter](), [deepseek](), [embedding](), [flash](), [google](), [gpu](), [latency](), [llm](), [llms](), [model](), [release]() ### AI overview DeepSeek V4.1 Flash is a larger LLM whose architectural changes aim to reduce serving memory and compute needs. The article highlights lower KV-cache consumption, improved prompt processing, and an N-gram-based conditional memory module. ### Source excerpt DeepSeek V4.1 Flash proves that just because you build a bigger model doesn't mean you need more GPUs to serve it ## DeepSeek V4.1 Flash now available on AI Gateway DevFeed: [DeepSeek V4.1 Flash now available on AI Gateway]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-09-09T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [deepseek](), [Artificial Intelligence](), [API](), [Language models](), [AI-assisted coding](), [Claude Code](), [codex](), [cursor]() Tags: [ai](), [ai-gateway](), [api](), [caching](), [codex](), [context-window](), [cursor](), [deepseek](), [responses](), [tool](), [vercel](), [vision]() ### AI overview DeepSeek V4.1 Flash is now available through Vercel's AI Gateway, offering native image understanding, a 1 million token context window, responses up to 384,000 tokens, reasoning, tool use, and prompt caching. Developers can use it in Claude Code, Codex, Cursor, and other coding agents with the model name deepseek/deepseek-v4.1-flash. ### Source excerpt DeepSeek V4.1 Flash is now available on AI Gateway with native image understanding. V4.1 Flash has vision support and accepts text and images in the same request, so you can ask questions about screenshots, read charts, and extract information from visual content. The model has a 1 million token context window and supports responses up to 384,000 tokens, along with reasoning, tool use, and prompt caching. Its new architecture processes input and generates output with separate components, reducing the active computation needed for each stage. Use deepseek/deepseek-v4.1-flash as the model name: To use it in Claude Code, Codex, Cursor, and more, install the latest Vercel CLI and run setup: Then select deepseek/deepseek-v4.1-flash in the agent. See the coding agents guide for details. Try DeepSeek V4.1 Flash in the model playground. AI Gateway provides a unified API for calling models, tracking usage and cost, and configuring retries, failover, and performance optimizations for higher-than-provider uptime. It includes built-in custom reporting, Zero Data Retention support, budgets for API keys, routing rules, and more. AI Gateway reflects provider pricing with no markup and does not charge a platform fee on inference, including on Bring Your Own Key (BYOK) requests. You can view all language models available on AI Gateway. Read more ## Optimize vLLM speculative decoding with FastMTP heads DevFeed: [Optimize vLLM speculative decoding with FastMTP heads]() Original publisher: [Read original article]() Author: Rahul Tuli Published: 2026-09-08T14:20:16Z Content type: article Language: en Sources: [Red Hat](), [Red Hat Developer]() Topics: [vllm](), [Inference](), [Large Language Model](), [Fine-tuning](), [model-deployment](), [deepseek](), [qwen]() Tags: [ai-inference](), [artificial-intelligence](), [data](), [deepseek](), [developer-tools](), [fine-tuning](), [hardware](), [inference](), [large-language-model](), [production](), [qwen](), [tokens](), [vllm]() ### AI overview This article explains how FastMTP-style fine-tuning improves vLLM speculative decoding. It describes using native multi-token prediction heads as speculators, adapting a single head for recursive multi-step drafting, extracting weights from verifier checkpoints, and producing vLLM-ready checkpoints without training from scratch. ### Source excerpt Autoregressive decoding makes large language model (LLM) inference memory-bandwidth bound: every token needs 1 full forward pass over billions of parameters, so the hardware spends most of its time moving weights rather than computing. MTP is a training objective: models like the DeepSeek and Qwen families learn to predict several future tokens at each position, which improves their data efficiency and quality. The post Optimize vLLM speculative decoding with FastMTP heads appeared first on Red Hat Developer. ## DeepSeek V4 Flash Vision Experimental now available on AI Gateway DevFeed: [DeepSeek V4 Flash Vision Experimental now available on AI Gateway]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-08-21T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [deepseek](), [Claude](), [Artificial Intelligence](), [Language models](), [Inference](), [Caching](), [Vercel](), [Claude Code](), [codex](), [cursor]() Tags: [ai](), [ai-gateway](), [caching](), [claude](), [claude-code](), [codex](), [coding-agents](), [cursor](), [deepseek](), [experimental](), [inference](), [language-models](), [playground](), [pricing](), [reasoning](), [tool](), [vercel](), [vision]() ### AI overview DeepSeek V4 Flash Vision Experimental is now available through Vercel AI Gateway. The model accepts images with text, supports tasks such as image description, screenshot text extraction, and chart analysis, and retains tool use, reasoning, and caching. The article also documents supported image formats, experimental-release caveats, coding-agent setup, playground access, and AI Gateway pricing. ### Source excerpt DeepSeek V4 Flash with vision is now available on AI Gateway. This model is an experimental version that accepts images alongside text. You can ask it to describe a picture, read text out of a screenshot, or work through a chart in the same request as your prompt. DeepSeek V4 Flash Vision Experimental now available on AI Gateway. Tool use, reasoning, and caching all work the same as before. Use deepseek/deepseek-v4-flash-vision-exp to get started: Images can be JPEG, PNG, GIF, or WebP. The format is read from the file's own bytes rather than its name or the mediaType you declare, so a mislabeled file still goes through. The -exp in the model ID marks this as an experimental release. Expect behavior to change, and keep a fallback model configured if it's on a production path. To use it in a coding agent, run vercel ai-gateway coding-agents setup to connect agents like Claude Code, Codex, OpenCode, Cursor, and Pi, then select deepseek/deepseek-v4-flash-vision-exp inside the agent. Try Deepseek V4 Flash Vision Experimental in the model playground. AI Gateway reflects provider pricing with no markup and does not charge a platform fee on inference, including BYOK requests. View all language models on AI Gateway to see more. Read more ## Agent Night panel recap: AI spending, open-source models, and the changing agent economy DevFeed: [Agent Night panel recap: AI spending, open-source models, and the changing agent economy]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-08-17T18:34:23Z Content type: article Language: en Sources: [WorkOS Blog]() Topics: [Artificial Intelligence](), [Open Source](), [deepseek](), [OpenAI]() Tags: [ai](), [anthropic](), [deepseek](), [engineering](), [harness](), [open-source](), [openai](), [recap]() ### AI overview A recap of WorkOS's Agent Night panel, where Jaya Gupta, Flo Crivello, and swyx discussed increased spending on AI tooling, progress in open-source models, and changing economics for AI agents. The article presents panelists' claims and observations, including Flo Crivello's view that AGI has arrived and reports of companies moving from OpenAI and Anthropic to open-source models. ### Source excerpt Jaya Gupta, Flo Crivello, and swyx on stage at Agent Night: 5x AI spend, open source getting real, harness engineering, skills, and December predictions. ## 🗓 This Week In AI Research (25-31 July 26) DevFeed: [🗓 This Week In AI Research (25-31 July 26)]() Original publisher: [Read original article]() Author: Dr. Ashish Bamania Published: 2026-08-07T01:00:43Z Content type: article Language: en Sources: [Into AI]() Topics: [releases](), [Machine learning](), [deepseek](), [foundation-models](), [Mixture of Experts (MoE)](), [lora](), [codex]() Tags: [ai](), [deep-learning](), [deepseek](), [lora](), [ml](), [moe](), [performance](), [releases](), [research]() ### AI overview A weekly roundup of AI research and releases covering DeepSeek-V4-Flash-0731, the Pangram 4 AI-text classification model, the OpenMLE system and its Frontis-MA1-35B agent, and the Metis memory foundation model. ### Source excerpt The top 10 AI research papers and releases this week. ## DeepSeek V4 Flash is 90% off through Novita on AI Gateway DevFeed: [DeepSeek V4 Flash is 90% off through Novita on AI Gateway]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-08-04T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [deepseek](), [API](), [API keys](), [Vercel](), [dashboards]() Tags: [ai](), [api](), [customers](), [deepseek](), [flash](), [model](), [playground](), [vercel]() ### AI overview DeepSeek v4 Flash is available at a 90% discount through Novita on Vercel's AI Gateway for Vercel Pro customers through August 11. Users can configure the specified DeepSeek model, prioritize Novita with fallback providers, create an API key, or test the model in the playground. ### Source excerpt DeepSeek v4 Flash is 90% off on AI Gateway when you route to Novita. The discount is available to Vercel Pro customers through August 11. To get the discounted rate, set the model to deepseek/deepseek-v4-flash or deepseek/deepseek-v4-flash-0731 and put Novita first with the order option: If Novita can't serve the request, it falls back to other providers at the standard rate. After August 11, the model stays available at standard rates with no markup. Create an API key in the AI Gateway section of your dashboard, or try the model first in its playground. Read more ## The Rise of Open-Weight AI Models and the Challenge to Commercial Frontier Labs DevFeed: [The Rise of Open-Weight AI Models and the Challenge to Commercial Frontier Labs]() Original publisher: [Read original article]() Author: Colin Eberhardt Published: 2026-07-27T00:00:00Z Content type: opinion Language: en Sources: [Scott Logic]() Topics: [Artificial Intelligence](), [AI Models](), [Open Source](), [OpenAI](), [anthropic](), [deepseek](), [ChatGPT](), [llama](), [Reinforcement learning](), [Transformer]() Tags: [2026](), [ai](), [ai-models](), [anthropic](), [artificial-intelligence](), [chatgpt](), [china](), [cost](), [deepseek](), [llama](), [open-source](), [openai](), [performance](), [privacy]() ### AI overview This article traces the development of commercial and open-weight AI models from OpenAI's founding through the rise of models predominantly emanating from China. It argues that open-weight models have narrowed or closed the capability gap with frontier labs and may offer comparable or better performance at lower cost, with greater ownership and data privacy. ### Source excerpt Chinese-led open-weight AI models have closed the capability gap with frontier labs like OpenAI and Anthropic, challenging the assumption that massive investments guarantee lasting competitive advantage. By 2026, open-weight alternatives offer equivalent or superior performance at a fraction of the cost, along with greater ownership and data privacy, fundamentally disrupting the commercial AI landscape. ## Open-weight models surge to 29% of volume, price per token flattens DevFeed: [Open-weight models surge to 29% of volume, price per token flattens]() Original publisher: [Read original article]() Author: Harpreet Arora Published: 2026-07-13T07:00:00Z Content type: article Language: en Sources: [Vercel News]() Topics: [Artificial Intelligence](), [data](), [anthropic](), [deepseek](), [OpenAI](), [Google]() Tags: [2026](), [ai](), [ai-gateway](), [anthropic](), [cost](), [deepseek](), [google](), [models](), [open](), [openai](), [production](), [release](), [tokens]() ### AI overview The July 2026 AI Gateway Production Index reports that gateway token volume grew 29% month over month in June while spending grew 27% and average token prices remained flat. Open-weight models reached 29% of token volume for under 4% of spend, while Anthropic captured 61% of spending. The report also compares model and provider leadership across text, image, and video workloads. ### Source excerpt AI Gateway Production Index -- July 2026 Every month, AI Gateway routes tens of trillions of tokens between production applications and AI labs, giving us a view of what AI usage actually looks like in today's enterprise. We publish that view here. See the Production Index reports published in May and June. July 2026 summary The July index reports on AI Gateway data collected in June 2026. AI Gateway token volume grew 29% month over month and spend grew 27%. The price per token was flat after rising almost 20% in May. Open-weight models ran 29% of gateway tokens, up from 11% in April, on under 4% of spend. DeepSeek reached 22.6% of token volume, in third place and less than two points behind Google, and GLM 5.2 broke into the gateway's top models by volume two weeks after release. Anthropic took 61% of gateway spend on 32% of tokens, and captured 72% or more of spend in every high-stakes use case. Each modality has a different leader. OpenAI's GPT Image generated 53% of images, ahead of Google's Nano Banana at 39%. Chinese labs took two-thirds of video spend, while xAI's Grok Imagine generated 42% of videos, the most of any model. Claude Fable 5 was released June 9 and reached 22% of Opus 4.8's request volume in four days. A US export-control directive suspended access for the rest of the month. AI investment kept climbing, and the average token price went flat In June, token volume and spend grew at nearly the same rate, 29% and 27%, while the price per token remained steady. The month before, spend had grown more than twice as fast as volume, driving the average token cost up by almost 20%. The market spent more overall, but not more per token. Since April, open-weight models have climbed from a ninth of all token volume to nearly a third, at about a tenth of the average token price on the gateway. That alone should have pulled the price per token down. But as cheap volume rose in June, so too did closed-weight frontier prices, up about 12% per token. They offset e ## DeepSeek models now available via Azure on AI Gateway DevFeed: [DeepSeek models now available via Azure on AI Gateway]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-06-11T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Azure](), [deepseek](), [API](), [vercel ai sdk](), [Inference](), [Routing (disambiguation)](), [Authentication]() Tags: [ai-gateway](), [api](), [api-keys](), [authentication](), [azure](), [deepseek](), [inference](), [routing](), [sdk]() ### AI overview Azure is now available as a provider for DeepSeek V4 Pro and V4 Flash through AI Gateway. Requests can use Azure for automatic routing and failover, or prefer it through provider options, without code changes. Existing Azure credentials can be used through Bring Your Own Key, while AI Gateway provides unified model access, usage and cost tracking, retries, reporting, and performance features without inference markups or platform fees. ### Source excerpt Azure is now a provider for DeepSeek V4 Pro and V4 Flash on AI Gateway. Requests to either model can route through Azure alongside the existing providers for another failover path. No code changes are required: default routing considers Azure automatically, and if a provider fails the gateway falls back through the remaining list. If you want requests to try Azure first, use order in the gateway provider options to prefer Azure while keeping the other providers as fallback for deepseek/deepseek-v4-pro or deepseek/deepseek-v4-flash in the AI SDK: If you have existing Azure credentials, you can bring your own key and AI Gateway will use it for requests routed to Azure. See API key authentication and BYOK for setup. AI Gateway provides a unified API for calling models, tracking usage and cost, and configuring retries, failover, and performance optimizations for higher-than-provider uptime. It includes built-in custom reporting, Zero Data Retention support, budgets for API keys, and more. AI Gateway reflects provider pricing with no markup and does not charge a platform fee on inference, including on Bring Your Own Key (BYOK) requests. Learn more about AI Gateway, view the AI Gateway model leaderboard or try it in our model playground. Read more ## DeepSeek enters the fight for token volume, Anthropic continues to dominate spend DevFeed: [DeepSeek enters the fight for token volume, Anthropic continues to dominate spend]() Original publisher: [Read original article]() Author: Eric Dodds Published: 2026-06-08T04:00:00Z Content type: article Language: en Sources: [Vercel News]() Topics: [deepseek](), [anthropic](), [Claude](), [data](), [Routing (disambiguation)](), [OpenAI]() Tags: [ai-gateway](), [amazon](), [anthropic](), [benchmarks](), [claude-code](), [cost](), [deepseek](), [models](), [openai](), [production](), [routing](), [tech-news](), [tokens]() ### AI overview The June 2026 AI Gateway Production Index reports that production token usage grew 20% month over month and spending grew 43%. DeepSeek's token share rose from below 1% to 17% after the launch of DeepSeek V4, while Anthropic's share of spending increased from 61% to 65%. The article highlights the growing use of lower-cost models in production and smarter routing across model tiers to improve spending efficiency. ### Source excerpt AI Gateway Production Index -- June 2026 Every month, AI Gateway routes tens of trillions of tokens between production applications and AI labs, giving us visibility into what AI usage actually looks like, separate from leaderboards and benchmarks. We publish the data monthly in the AI Gateway production index. June 2026 summary The July index reports on AI Gateway data collected in May 2026. Total AI Gateway tokens grew +20% MoM; total spend grew +43% MoM. Customers paid almost 20% more per token on average than in April. DeepSeek's share of tokens jumped from under 1% to 17% in a single month, while its share of spend stayed near 1%. Anthropic's share of spend grew from 61% to 65% in May, holding 70-80% of spend across every high-stakes use case (AI app generation, back office agents, and coding agents). Cost-consciousness meant smarter routing between low-cost and frontier models. Customers got more deliberate about which model did which work, while overall usage kept climbing. Last month, headlines about blown token budgets dominated tech news: Uber burned through its annual Claude Code budget shortly after Q1 and Amazon shut down KiroRank to curb unproductive tokenmaxxing. While runaway cost is a real problem, this month's report shows that spend on production use cases still increased. Two insights emerged from AI Gateway data in May: Low-cost models entered production: New models shipped at price points that made the established labs look even more expensive, and they are capable enough to enter the mix in production. Spend is increasing, but with smarter model mixes: Teams are still increasing token budgets, but they are implementing smarter routing strategies to get more value out of every dollar. Low-cost models saw significant production volume for the first time From February to April, volume distribution across labs on AI Gateway changed slowly, but in May, DeepSeek V4's launch completely shifted token share. The low-cost end of the market that barely ex ## DeepSeek V4, LeCun's Bet Against LLMs, and Lovable's Self-Improving Agent - The Tokenizer Edition #30 DevFeed: [DeepSeek V4, LeCun's Bet Against LLMs, and Lovable's Self-Improving Agent - The Tokenizer Edition #30]() Original publisher: [Read original article]() Author: Sairam Sundaresan Published: 2026-06-04T13:31:46Z Content type: article Language: en Sources: [Gradient Ascent]() Topics: [Artificial Intelligence](), [Large Language Model](), [deepseek](), [Machine learning](), [World models](), [Benchmark]() Tags: [ai](), [ai-ml](), [benchmarks](), [deepseek](), [diffusion](), [llms](), [research](), [training]() ### AI overview This English-language weekly roundup surveys AI and machine-learning resources, including papers on text-to-image generation, reinforcement learning for research agents, spatial reasoning, and benchmarks; videos about DeepSeek V4, JEPA world models, and agent improvement; and reads on FP8 KV-cache quantization, diffusion generation, and delegated-work fidelity. ### Source excerpt This week's most valuable AI resources ## Sort providers by cost, latency, or throughput on AI Gateway DevFeed: [Sort providers by cost, latency, or throughput on AI Gateway]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-05-15T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Inference Performance](), [Routing (disambiguation)](), [Latency](), [gpt-oss](), [deepseek]() Tags: [ai](), [ai-gateway](), [deepseek](), [generation](), [gpt-oss](), [latency](), [model](), [models](), [routing](), [speed](), [tokens]() ### AI overview AI Gateway now supports sorting model providers by cost, time to first token, or tokens-per-second throughput. Ranking is calculated at request time, works with filtering and provider-order controls, and exposes routing decisions in response metadata. ### Source excerpt You can now sort the providers behind a model by cost, time to first token (TTFT), or throughput (TPS) in AI Gateway. The default provider order blends provider reliability, quality of model output, cost, and speed of response. You can now use sort for explicit control over ranking criteria. For models with many providers and noticeable cost or speed variation, you can use sort to optimize on your dimension of choice. Ranking is computed at request time, so newly added providers, price changes, and shifts in observed latency or throughput flow through automatically without any code changes. Set sort on providerOptions.gateway to one of the three values: Value Description Direction When to use 'cost' Sort by the provider's listed input price per million tokens Lowest price first High-volume, cost-sensitive work 'ttft' Sort by median time to first token, in ms Lowest latency first Latency-sensitive workloads where response speed matters 'tps' Sort by median tokens per second throughput Highest first Long-output generation where total response time matters most Basic usage Use sort to ensure optimizing for your metric of choice. In this example, AI Gateway has over five providers for GPT OSS 120B with different prices, so sorting by cost is a useful option for requests that want to route through the lowest price provider. Providers are tried in sort order. Fallback to the next provider only happens when the higher-ranked one is unavailable. Combine with other routing controls sort is compatible with other gateway routing options like Zero Data Retention (ZDR). The example below uses deepseek/deepseek-v4-pro for an interactive request where latency and data retention matter: AI Gateway filters to only providers for Deepseek V4 Pro that have zero data retention, and then sorts the remaining providers by time to first token (TTFT). sort also composes with order: providers listed in order are promoted to the front, and the remaining providers follow the requested sort crit ## DwarfStar 4 and the Future of Local AI Model Support DevFeed: [DwarfStar 4 and the Future of Local AI Model Support]() Original publisher: [Read original article]() Published: 2026-05-14T22:22:45Z Content type: opinion Language: en Sources: [Antirez]() Topics: [Local AI](), [deepseek](), [Inference](), [Frontier Model](), [DGX Spark](), [GPU](), [Large Language Model]() Tags: [ai](), [deepseek](), [dgx-spark](), [frontier-model](), [gpu](), [inference](), [llms](), [local-ai]() ### AI overview The author reflects on DwarfStar 4's rapid popularity and attributes it to demand for a focused local AI experience, capable hardware, quantization, and recent local AI advances. They describe a plan to support the best practically fast open-weights model over time, with possible specialized variants for coding, legal, and medical use. ### Source excerpt I didn't expect DwarfStar 4 (https://github.com/antirez/ds4) to become so popular so fast. It is clear that there was a need for single-model integration focused local AI experience, and that a few things happened together: the release of a quasi-frontier model that is large and fast enough to change the game of local inference, and the fact that it works extremely well with an extremely asymmetric quants recipe of 2/8 bit, so that 96 or 128GB of RAM are enough to run it. And, of course: all the experience produced by the local AI movement in the latest years, that can be leveraged more promptly because of GPT 5.5 (otherwise you can't build DS4 in one week -- and even with all this help you need to know how to gently talk to LLMs). The last week was funny and also tiring, I worked 14 hours per day on average. My normal average is 4/6 since early Redis times, but the first few months of Redis were like that. So, what's next? Is this a project that starts and ends with DeepSeek v4 Flash? Nope, the model can change over time. The space will be occupied, in my vision, by the best current open weights model that is *practically fast* on a high end Mac or "GPU in a box" gear (like the DGX Spark and other similar setups). I bet that the next contender is DeepSeek v4 Flash itself, in the new checkpoint that will be released and, hopefully, a version specifically tuned for coding, and who knows, other expert-variants (not in the sense of MoE experts) maybe. For local inference, to have a ds4-coding, ds4-legal, ds4-medical models make a lot of sense, after all. You just load what you need depending on the question. It is the first time since I play with local inference (I play with it since the start) that I find myself using a local model for serious stuff that I would normally ask to Claude / GPT. This, I think, is really a big thing. It is also the first time that using vector steering I can enjoy an experience where the LLM can be used with more freedom. DeepSeek v4 Flash ## DeepSeek-V4: a million-token context that agents can actually use DevFeed: [DeepSeek-V4: a million-token context that agents can actually use]() Original publisher: [Read original article]() Author: ben burtenshaw Published: 2026-04-24T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [deepseek](), [long-context](), [context window](), [Inference](), [Cache](), [Benchmark](), [model-deployment](), [AI Chat](), [Open Source]() Tags: [agentic](), [agents](), [artificial-intelligence](), [benchmark](), [cache](), [community](), [context-window](), [deepseek](), [gpu](), [inference](), [llm](), [long-context](), [moe](), [open-source](), [research]() ### AI overview DeepSeek-V4 is presented as an open model designed for long-running agentic workloads. The article explains how its architecture reduces the inference FLOPs and KV cache memory required for million-token contexts, and describes post-training choices intended to improve extended tool-use trajectories. It highlights Compressed Sparse Attention and a lightning indexer as mechanisms behind the efficiency gains, with comparisons against DeepSeek-V3.2 and grouped-query attention. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Heroku AI adds a flexible standard plan and expands its managed model catalog DevFeed: [Heroku AI adds a flexible standard plan and expands its managed model catalog]() Original publisher: [Read original article]() Author: Anush DSouza Published: 2026-02-19T17:08:20Z Content type: release Language: en Sources: [Heroku]() Topics: [Heroku AI](), [Heroku](), [model-deployment](), [Claude](), [deepseek](), [cohere]() Tags: [claude](), [cohere](), [deepseek](), [deployment](), [heroku](), [heroku-ai](), [inference](), [managed-inference-and-agents](), [models](), [news](), [updates]() ### AI overview Heroku introduces a standard plan for Managed Inference and Agents that lets developers access supported models through one add-on and API key, while adding Claude 4.6, new open-weight models, and Cohere Embed V4. ### Source excerpt Heroku is introducing significant updates to Managed Inference and Agents. These changes focus on reducing developer friction, expanding model catalogue, and streamlining deployment workflows. More flexibility with the new standard plan Until now, Heroku's model-based plans required developers to provision a specific add-on for a specific model. This created significant operational overhead. If you wanted [...] The post Whats New in Heroku AI: New Models and a Flexible Standard Plan appeared first on Heroku. ## The Future of the Global Open-Source AI Ecosystem: From DeepSeek to AI+ DevFeed: [The Future of the Global Open-Source AI Ecosystem: From DeepSeek to AI+]() Original publisher: [Read original article]() Author: Adina Yakefu; Irene Solaiman Published: 2026-02-03T15:03:19Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Artificial Intelligence](), [Open Source](), [Open Source Models & Datasets](), [deepseek](), [qwen](), [hugging face](), [Meta](), [Deployment]() Tags: [ai](), [article](), [blog](), [china](), [deepseek](), [deployment](), [ecosystem](), [hugging-face](), [infrastructure](), [integration](), [meta](), [model](), [models](), [open-source](), [qwen](), [updates]() ### AI overview This third and final article in a series examines the trajectories of prominent Chinese AI organizations after the January 2025 "DeepSeek Moment." It argues that open source is becoming the dominant approach for Chinese AI organizations, supported by the sharing of models, papers, techniques, and deployment infrastructure. The article highlights DeepSeek, Qwen, and other organizations in the emergence of a collaborative Chinese and global open-source AI ecosystem. ### Source excerpt This is the third and final blog in a three-part series on China's open source community's historical advancements since January 2025's "DeepSeek Moment." The first blog on strategic changes and open artifact growth is available here, and the second blog on architectural and hardware shifts is available here. In this third article, we examine paths and trajectories of prominent Chinese AI organizations, and posit future directions for open source. ## One Year Since the "DeepSeek Moment" DevFeed: [One Year Since the "DeepSeek Moment"]() Original publisher: [Read original article]() Author: Adina Yakefu; Irene Solaiman Published: 2026-01-20T15:02:10Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [deepseek]() Tags: [ai](), [ai-development](), [blog](), [china](), [compute](), [deepseek](), [developers](), [ecosystem](), [global](), [model](), [models](), [open](), [open-source](), [release](), [research]() ### AI overview The article reflects on the year after DeepSeek R1, arguing that it accelerated open AI models and a broader open-source ecosystem, particularly in China. It describes lower barriers to advanced AI capabilities, rapid iteration, and growing global adoption of open models. ### Source excerpt The first blog addresses strategic changes and the explosion of new open models and open source players. The second covers architectural and hardware choices largely by Chinese companies made in the wake of a growing open ecosystem, available here. The third analyzes prominent organizations' trajectories and the future of the global open source ecosystem, available here. ## On the Shifting Global Compute Landscape DevFeed: [On the Shifting Global Compute Landscape]() Original publisher: [Read original article]() Author: Tiezhen WANG; Irene Solaiman Published: 2025-10-29T13:56:45Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [AI Development](), [Artificial Intelligence](), [Nvidia](), [Open Source](), [Inference](), [CUDA](), [Optimization](), [huawei](), [model-deployment](), [deepseek](), [grpo](), [Deployment]() Tags: [ai](), [ai-development](), [ai-models](), [china](), [cuda](), [culture](), [deepseek](), [deployment](), [developers](), [efficiency](), [grpo](), [huawei](), [inference](), [nvidia](), [open-source](), [optimization](), [production](), [training]() ### AI overview The article examines how China's progress in open-weight AI models is coinciding with rapid domestic AI chip development. It discusses Huawei Ascend and Cambricon chips, reduced reliance on NVIDIA, compute-efficiency innovations such as DeepSeek's MLA and GRPO, and the emergence of software alternatives to CUDA. ### Source excerpt The status quo of AI chip usage, that was once almost entirely U.S.-based, is changing. China's immense progress in open-weight AI development is now being met with rapid domestic AI chip development. In the past few months, highly performant open-weight AI models' inference in China has started to be powered by chips such as Huawei's Ascend and Cambricon, with some models starting to be trained using domestic chips. ## Explainer: K2 & Math Olympiad Golds DevFeed: [Explainer: K2 & Math Olympiad Golds]() Original publisher: [Read original article]() Published: 2025-07-19T00:00:00Z Content type: article Language: en Sources: [Tim Kellogg]() Topics: [Artificial Intelligence](), [multi-agents](), [Chain-of-thought](), [Claude](), [deepseek](), [Model Context Protocol](), [OpenAI](), [computer-use]() Tags: [agentic](), [agents](), [ai](), [benchmarks](), [chain-of-thought](), [context-engineering](), [cost](), [explainer](), [multi-agent](), [multi-agents](), [openai](), [tokens](), [tools]() ### AI overview This explainer reviews major AI-agent developments from January through July 2025, focusing on K2 and the International Math Olympiad gold result. It argues that K2's strong agentic performance without a long chain-of-thought trace raises questions about whether extended thinking is necessary for effective agents, while noting that shorter reasoning can reduce token costs. ### Source excerpt the best way to emphasize the importance of this week's developments is to go all the way back to January and see how we got here. ## Building a streaming DeepSeek-R1 app on Azure DevFeed: [Building a streaming DeepSeek-R1 app on Azure]() Original publisher: [Read original article]() Author: Pamela Fox (noreply@blogger.com) Published: 2025-04-02T18:15:00Z Content type: tutorial Language: en Sources: [Pamela Fox]() Topics: [Artificial Intelligence](), [deepseek](), [Azure](), [Deployment](), [Infrastructure as code](), [Python](), [Serverless](), [Inference]() Tags: [ai](), [azure](), [code](), [deepseek](), [deployment](), [inference](), [infrastructure-as-code](), [openai](), [python](), [serverless](), [streaming]() ### AI overview A tutorial on building a streaming DeepSeek-R1 application on Azure. It explains how to deploy the model as a serverless Azure AI Services resource, use keyless authentication, connect from Python, and present reasoning thoughts separately from the final answer. ### Source excerpt Update: The approach has slightly changed (in a good way!). Read this Microsoft Learn article for an updated guide. This year, we're seeing the rise in "reasoning models", models that include an additional thinking process in order to generate their answer. Reasoning models can produce more accurate answers and can answer more complex questions. Some of those models, like o1 and o3, do the reasoning behind the scenes and only report how many tokens it took them (quite a few!). The DeepSeek-R1 model is interesting because it reveals its reasoning process along the way. When we can see the "thoughts" of a model, we can see how we might approach the question ourself in the future, and we can also get a better idea for how to get better answers from that model. We learn both how to think with the model, and how to think without it. So, if we want to build an app using a transparent reasoning model like DeepSeek-R1, we ideally want our app to have special handling for the thoughts, to make it clear to the user the difference between the reasoning and the answer itself. It's also very important for a user-facing app to stream the response, since otherwise a user will have to wait a very long time for both the reasoning and answer to come down the wire. Here's an app with streamed, collapsible thoughts: You can deploy that app yourself from github.com/Azure-Samples/deepseek-python today, or you can keep reading to see how it's built. Deploying DeepSeek-R1 on Azure We first deploy a DeepSeek-R1 model on Azure, using Bicep files (infrastructure-as-code) that provision a new Azure AI Services resource with the DeepSeek-R1 deployment. This deployment is what's called a "serverless model", so we only pay for what we use (as opposed to dedicated endpoints, where the pay is by hour). var aiServicesNameAndSubdomain = '${resourceToken}-aiservices' module aiServices 'br/public:avm/res/cognitive-services/account:0.7.2' = { name: 'deepseek' scope: resourceGroup params: { name: aiServi ## Open R1: Update #4 DevFeed: [Open R1: Update #4]() Original publisher: [Read original article]() Author: Leandro von Werra; Vaibhav Srivastav; Daniel Vila; Yacine Jernite Published: 2025-03-26T18:47:29Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [deepseek](), [Benchmark](), [Code](), [coding](), [math](), [post-training](), [Front end]() Tags: [benchmark](), [benchmarks](), [code](), [coding](), [deepseek](), [function-calling](), [math](), [post-training](), [release](), [update]() ### AI overview The article reports on a new DeepSeek model that updates DeepSeek-V3, retains its architecture, and adopts an MIT license. It describes improvements in instruction following, coding, mathematics, front-end web development, writing, search, and function calling, supported by stronger benchmark results. ### Source excerpt This week, a new model from DeepSeek silently landed on the Hub. It's an updated version of DeepSeek-V3, the base model underlying the R1 reasoning model. There isn't much information shared yet on this new model, but we do know a few things! The model has the same architecture as the original DeepSeek-V3 and now also comes with an MIT license, while the previous V3 model had a custom model license. ## Big LLMs weights are a piece of history DevFeed: [Big LLMs weights are a piece of history]() Original publisher: [Read original article]() Published: 2025-03-16T11:56:33Z Content type: opinion Language: en Sources: [Antirez]() Topics: [Large Language Model](), [data](), [deepseek](), [Internet]() Tags: [data](), [deepseek](), [history](), [internet](), [llms]() ### AI overview The article argues that the disappearance of web pages and other digital materials makes preservation increasingly difficult. It presents publicly released LLM weights, including DeepSeek V3, as imperfect compressed views of Internet knowledge and argues that preserving those weights should complement support for the Internet Archive and similar institutions. ### Source excerpt By multiple accounts, the web is losing pieces: every year a fraction of old web pages disappear, lost forever. We should regard the Internet Archive as one of the most valuable pieces of modern history; instead, many companies and entities make the chances of the Archive to survive, and accumulate what otherwise will be lost, harder and harder. I understand that the Archive headquarters are located in what used to be a church: well, there is no better way to think of it than as a sacred place. Imagine the long hours spent by old programmers hacking with the Z80 assembly on their Spectrums. All the discussions about the first generation of the Internet. The subcultures that appeared during the 90s. All things that are getting lost, piece by piece. And what about the personal blogs? Pieces of life of single individuals that dumped part of their consciousness on the Internet. Scientific papers and processes that are lost forever as publishers fail, their websites shut down. Early digital art, video games, climate data once published on the Internet and now lost, and many sources of news, as well. This is a known issue and I believe that the obvious approach of trying to preserve everything is going to fail, for practical reasons: a lot of efforts for zero economic gains: the current version of the world is not exactly the best place to make efforts that cost a lot of money and don't pay money. This is why I believe that the LLMs' ability to compress information, even if imprecise, hallucinated, lacking, is better than nothing. DeepSeek V3 is already an available, public lossy compressed view of the Internet, as other very large state of-art models are. This will not bring back all the things we are losing, and we should try hard supporting The Internet Archive and other similar institutions and efforts. But, at the same time, we should focus on a much simpler effort: to make sure that the weights of LLMs publicly released do not get lost, and also to make sure that th ## Open R1: Update #3 DevFeed: [Open R1: Update #3]() Original publisher: [Read original article]() Author: Guilherme Penedo; Lewis Tunstall; Anton Lozhkov; Hynek Kydlicek; Edward Beeching; Loubna Ben Allal; Quentin Gallouédec; Leandro von Werra; Agustín Piqueres Lajarín; Nathan Habib Published: 2025-03-11T20:40:47Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Programming](), [Code](), [Benchmark](), [dataset](), [C++](), [Python](), [Chain-of-thought](), [deepseek]() Tags: [2025](), [benchmark](), [c-plus-plus](), [chain-of-thought](), [code](), [contests](), [data](), [deepseek](), [evaluation](), [model](), [models](), [performance](), [programming](), [reasoning](), [solutions](), [training](), [update]() ### AI overview Open R1 Update #3 presents CodeForces-CoTs, a dataset of nearly 100,000 DeepSeek-R1 chain-of-thought samples for generating competitive-programming solutions in C++ and Python. It also introduces the IOI benchmark and OlympicCoder models fine-tuned on this data, reporting strong performance on challenging IOI problems. ### Source excerpt Over the last few weeks, we have focused our efforts on reproducing the competitive programming (code reasoning) aspects of the DeepSeek-R1 recipe. In this post, we are excited to share: - The construction of CodeForces-CoTs: a dataset of nearly 100k high-quality samples distilled from R1 to produce solutions in C++ and Python. - The IOI benchmark: a new benchmark of challenging problems from the 2024 International Olympiad in Informatics (IOI). [Next page]()