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Oct 16, coverage framed Moonshot's Kimi K3.1 as a name without a released model, and one report said a new Chinese export-license requirement for large AI models could affect it. Kimi K3.1 Pro launched Oct 24, and Moonshot reset and then restored Pro quotas after user complaints on Oct 26-27.","stale":false,"whats_new":"On Oct 29, ZenMux introduced Kimi K3.1 Pro for long-context workflows, the second third-party host reported after NEAR AI Cloud on Oct 28.","why_it_matters":"Two third-party hosts in two days give a possible route to Kimi K3.1 Pro outside Moonshot's quota-limited API, but pricing, limits and data terms are not in the reports.","take_for_builders":"Rerun your own evals on Kimi K3.1 Pro before routing traffic, keep a fallback model until Moonshot publishes stable quota limits, and compare NEAR's and ZenMux's terms once published before using either as a second route.","status":{"state":"Launched · third-party hosting","tone":"now","changed":"2026-10-29","detail":"Moonshot restored Pro quotas after an apology; NEAR AI Cloud and ZenMux now list the model, and a stable quota policy has not been reported.","track":[{"label":"unreleased","detail":"Oct 16","tone":"neutral","weight":15},{"label":"release prep","detail":"Oct 23","tone":"rising","weight":15},{"label":"launched","detail":"Oct 24","tone":"launch","weight":20},{"label":"quota reset + apology","detail":"Oct 26-27","tone":"alert","weight":25},{"label":"third-party hosting","detail":"Oct 28 → now","tone":"now","weight":25}]}}},{"slug":"deepseek-flash","label":"DeepSeek Flash","item_count":12,"day_count":9,"source_count":3,"first_seen":"2026-10-11T17:00:10+00:00","last_updated":"2026-10-29T14:00:00+00:00","latest_title":"Rival.io Launches GraphX, Running DeepSeek V4 Flash on Just Eight NVIDIA L4 GPUs","days":["2026-10-11","2026-10-14","2026-10-16","2026-10-17","2026-10-18","2026-10-19","2026-10-20","2026-10-26","2026-10-29"],"member_sids":["0eb5aab42ce7a0b7","2542a0a265628285","6e859d00d3f1f59e","cbe41f424a1a6b13","b803fa0d4c28e1bf","f045e22fb623d831","a50f300013a679ed","c17f7f361be66b3c","20f6398bac4d28c9","a379ec2597bb3939","a9c17daa5dbfcba0","122ec7cea45d8a1a"],"member_urls":["https://news.google.com/rss/articles/CBMizAFBVV95cUxNMmV0X3hDcW12NDFmN3RRem9ieGxsSGZOdm9kaHRvc2dCN2h1NVdGTTBXRnpyQnk5YTFDcDRfcjl6SXNlb0hJajlrMnlGYmxYcngtdFJJUlB2dFFTdDdFYmFGUS1mVFN4eWU5Z1JnYkZFSXhpbHlmRUFaWThKbWhJVjJ3aHpQN3dPaUdEcHNDallUeEctNnZ6NUhPUlQyTVRUSnRaZ1QtaFk3anJ2dExXdENJdG5RaHhrdDdJXzBWUVptNUZLNmEtSE9ndjM?oc=5","https://news.google.com/rss/articles/CBMid0FVX3lxTFBPYmFLcFNqS0FGb0M4TDEtb0FmNGpLQjlkajFqTGswTHFNWUZqeHBGYlpkSVp0S21mLTRTc3J6SU1WNE9Ddm5kZEhtMDE0cFgzZ1pISEFMdHBHQ1g4NzV2UEgwZzhJVXNKYV9RcEVFSzRmREpEaXg0?oc=5","https://news.google.com/rss/articles/CBMijAFBVV95cUxQQmFGQ25uc2ZfRU1Ja2hkTmVNeWJjRzBxX2x3clBhb1I5RUVLNUdHNkdycFB6WXFiYmtkV1ZPbnltTWVoWTYxS0lSa3o1V2JJZmpiMXVpbE1ZeE1xcWxqN2ZZTENMZVlTRTI5U0FwcGNMY1d3X2U4NTU2ZXF3S3ZoZjNNUjF4aXVPcERKTA?oc=5","https://news.google.com/rss/articles/CBMid0FVX3lxTFBXNVE4V2hiNHhyRk5qeGZ2aU5lM3pjLVpELTl1VVJUc1BYeF80NTZrcVhOalJUTWlfN0JETUs2ODRYdXdoVHotZllhdEpvdjJnV0NwX0lFaU5SU25DMk84RWp4NDVJTFdYTTFNSFNwNExSNjR5eVlJ?oc=5","https://news.google.com/rss/articles/CBMipwFBVV95cUxOZU1qMWZqZDNjZGluci0wUC13OGpKbHNpT2ZjTUlFbGlNZWtacUljaFNnN0x6UG1ObE9BRktoVklFWEFDbmJGTGh5U0ZaQm00QzRPRE5Tc1ZoMDctc09YeUR3SnhxRWxYY2hDbmNPNmo0V0NJZy1XSmdTOWJvYW9hU0dBcHFmbW5NbG1mWFVZNExoRmZvaEFxODlFeC13YVpUQmx2a29wNA?oc=5","https://news.google.com/rss/articles/CBMi0gFBVV95cUxQMjBtNG5jOGtJNDMtWHYtVXo1RU44LWJlNTZrNkhxajRYd0JOMkdXLXV3ZlZwQTVidkoyWjJKbUNvS0U2UXdRdjZmNE9KWHhlTUR0Y2xmWWhTRm9MbFpuQm5NUEtyejJqWXZhYmRjWmdVTFZ0RFlVR2JwMTNqOUgtb3NxeHU2dUJVcHpTaUN5WFFCQkIzWXZhZGJVVXpUV0RfQUNxNjNLSngteWVuaVVEckhkWmNVbjF1QVQ4b2FoTHYxVmh0eko4T0ZKSG91c3ZWTlE?oc=5","https://news.google.com/rss/articles/CBMidkFVX3lxTFBDa0p6S1dIZGJ6V1dhSTFwbUJvT1VfODRyamxXWU5pX01PUDdySU5YZTRSdjEtU2tGNGJ0S3hTWEZnaGVXcHFCRVhmNjc1ZXVHd2QwUUJKLVA3cV8tVm5JYnlyNlFJVXhLZUZQTUhOeXFtbjNTbEE?oc=5","https://news.google.com/rss/articles/CBMikwFBVV95cUxQMW5VYU5mbW5FTmUxVlpuU3dNU0pWdXpGU19ublRrRm1HUFNRS0pBSlZzdjQ4VEQ3dlVtYXYzcmhPUUtMdzNCSmUtZWxJU3lDNTNMdU1uSHBQSWxwWmZzQjhiZktGeldHLWlLa2VoN3RBblN6VnhkMzdoanJ2TTVqZ2NSOHNSUVkzd1QxZmwxMENseXM?oc=5","https://news.google.com/rss/articles/CBMirwFBVV95cUxNOWxwekdueE83N18yaE1YbnlTRmJSSHpzYTBPQUlRLVhONzNwVFRwSWZMU29jY1hnTkNBTDdNOEZSSlB1SlNBNV9FTWQ0Nmk3WGNNVWZOR0Y1MEFTc1g2aEZUQkRwOUdQMWlwTzZHVDhib25aLXNrRGVoV1pfWXBfWGdoTXl2WS1fRzJFektWR1gxb2JuZmRhWGk4MlphelZNNm1PNXN2TXRPN2s5UU1J?oc=5","https://www.interconnects.ai/p/latest-open-artifacts-25-deepseek","http://arxiv.org/abs/2610.35671v1","https://news.google.com/rss/articles/CBMinAFBVV95cUxOWkd5NkJONUV6dFBKcHA3cUI1Nm5UUmJMV2JzU3pwbk1iVXpoNUdhSWF6WC05alBZYTdrNkMydUx0LVZPNU11YjJBanJiZHpmMkczVGlqYVZIeXVTTHRKcEV2UzJaaDUzdVlIQktnZXlKWjQ0UFh2YVdwWmozQ0ZtRTVqTEw3NDYyMHpHSlBnVElLdk50dFZBSFczeHc?oc=5"],"editorial":{"tldr":"DeepSeek V4.1 Flash reached hosted APIs and AMD benchmarks by Oct 14, and OrcaRouter priced it against rival models on Oct 16. The Flash-1017 update followed Oct 17-19 with stronger coding, cheaper inference and a public search API, and an independent risk paper appeared Oct 26.","stale":false,"whats_new":"Rival.io launched GraphX on Oct 29, claiming to run DeepSeek V4 Flash on just eight NVIDIA L4 GPUs, a self-hosting cost claim the coverage does not yet back with benchmarks.","why_it_matters":"Flash is now cheaper per task, has an independent safety evaluation, and has a vendor claiming a small-GPU serving footprint. Hosting cost and risk posture can both be weighed, though the footprint claim is unverified.","take_for_builders":"Rerun your agent eval and cost-per-task numbers on Flash-1017 before switching, read the Oct 26 risk evaluation before granting tool access, and wait for GraphX throughput data before sizing any L4 deployment.","status":{"state":"Shipped · Flash-1017 under evaluation","tone":"now","changed":"2026-10-29","detail":"Flash-1017 is out; a safety paper and a low-GPU serving claim are the newest evidence."}}},{"slug":"artificial-intelligence","label":"Artificial Intelligence","item_count":3,"day_count":3,"source_count":3,"first_seen":"2026-10-19T04:47:33+00:00","last_updated":"2026-10-29T11:41:00+00:00","latest_title":"How to Make Artificial Intelligence More Environmentally Sustainable","days":["2026-10-19","2026-10-28","2026-10-29"],"member_sids":["7ee71f432dd44354","9c4b3f7ffe57c3a6","2a3d1cccfaf6c83d"],"member_urls":["https://news.google.com/rss/articles/CBMiT0FVX3lxTE1tNi1MMkNiWURsdGdqRzZaazhPZHNYV09lSWxYM3B5Um84Tl85OGJ2X3dvanhaM3ZJSzBlWkg3UFBsdkg1blc5bG9zOVNacXM?oc=5","http://arxiv.org/abs/2610.38361v1","https://www.infoq.com/news/2026/10/AI-environmentally-sustainable/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=AI%2C+ML+%26+Data+Engineering"],"editorial":{"tldr":"This page groups items by the generic phrase \"artificial intelligence\", so it is a reading list rather than one thread. Earlier items covered AI oversight bodies; the current window opens with a DeepSeek model upgrade on Oct 19.","stale":false,"whats_new":"Oct 29: InfoQ argues sustainability should be a top-level architectural concern, citing rising electricity use at Microsoft and Google and IT at roughly 2-4% of global emissions.","why_it_matters":"Energy is becoming a design input next to cost and latency; an LLM-search security audit also found weak evidence on /llms.txt effects.","take_for_builders":"Track energy and carbon per workload alongside cost and latency; do not rely on /llms.txt as a security control until isolated evidence exists."}},{"slug":"web-search","label":"Web Search","item_count":4,"day_count":4,"source_count":4,"first_seen":"2026-10-17T20:30:24+00:00","last_updated":"2026-10-29T07:14:00+00:00","latest_title":"Cloudflare Introduces Web Search API for AI Applications","days":["2026-10-17","2026-10-26","2026-10-27","2026-10-29"],"member_sids":["dd757ca1b3c6b43c","5f97c21b490b1ce9","e3636bb8c76777d4","4babea341ec6b4e6"],"member_urls":["https://news.google.com/rss/articles/CBMiigFBVV95cUxON1poTVZpWnR5dFNydER0dTFQVmZmZnBDTFhrYl8xWEpiWmNnRzBYZWFMdjAwNjR4SEljQzUzZFN5aVRZbW05eHE2bDN4amJ0c2s3TkI4dFl6R2QzbmtOQWFxM2pWTmZzd1BtM1lhd2gzWXVTRHNJT0JHR21GSVZ2WHl2bG02UFIwMHc?oc=5","http://arxiv.org/abs/2610.35150v1","https://github.com/psinglap/websearch-api-eval","https://www.infoq.com/news/2026/10/cloudflare-web-search-api/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=AI%2C+ML+%26+Data+Engineering"],"editorial":{"tldr":"DeepSeek priced agent web search at $0.75 per 1,000 calls on Oct 17, making search a metered line item. A long-horizon eval paper (Oct 26) and a 4-API community benchmark (Oct 27) followed.","stale":false,"whats_new":"Cloudflare launched a beta Web Search API through AI Gateway, with unified logging, access controls and billing, via REST and Workers.","why_it_matters":"Search is moving into the gateway layer where agent traffic is already logged and billed, so provider choice becomes a gateway config question, but the pricing and benchmark claims are still unverified.","take_for_builders":"If you already route model traffic through a gateway, test its search option against your current API on your own production queries, tracking cost per 1,000 calls and answer quality before switching.","status":{"state":"Developing","tone":"rising","changed":"2026-10-29","detail":"A second vendor now offers metered agent search, but sources are thin and the price point and benchmark are not independently confirmed."}}},{"slug":"large-language","label":"Large Language","item_count":15,"day_count":7,"source_count":7,"first_seen":"2026-10-16T14:26:44+00:00","last_updated":"2026-10-29T00:00:00+00:00","latest_title":"Welcome Le Chonk: Mistral Large 4 on Hugging Face","days":["2026-10-16","2026-10-20","2026-10-21","2026-10-22","2026-10-26","2026-10-27","2026-10-29"],"member_sids":["ade0f523550f640a","06d8677dec3b3c21","16071b3c73317004","276a486ae6eefe6b","590975aa217085b7","8debe4581e047c88","f07fa8132755e14c","c888971c48a268c3","5f97c21b490b1ce9","d5cd3db9b5f4a99b","a2f964aa6fcbf7ea","200271de2a2f52aa","9c42d9612d92f7f3","37f6c0c263213efd","b6b30743068c2627"],"member_urls":["http://arxiv.org/abs/2610.23525v1","http://arxiv.org/abs/2610.27868v1","http://arxiv.org/abs/2610.29895v1","http://arxiv.org/abs/2610.30435v1","https://news.google.com/rss/articles/CBMijwFBVV95cUxPN1hlSUJENkV6eFRCbEVzMl8zU3p4YjBTQ1dra0RTU2QyWnh3ZlVYZDB6cUNqdXNtUjFDZFZNYkpIVy1DV0F6S0tNdXpFZ3BkMWZLYXZaZ3VOUkczanNSS2RDUkZYWmhqUVZwRXhPaHB5LTc1dDVRM3IxSjBGNnl4djlEV2NCeV9uVWh5UWN5VQ?oc=5","http://arxiv.org/abs/2610.30676v1","http://arxiv.org/abs/2610.31397v1","http://arxiv.org/abs/2610.31529v1","http://arxiv.org/abs/2610.35150v1","http://arxiv.org/abs/2610.35530v1","http://arxiv.org/abs/2610.35829v1","http://arxiv.org/abs/2610.36734v1","http://arxiv.org/abs/2610.37859v1","https://vllm.ai/blog/2026-10-29-mistral-large-4","https://huggingface.co/blog/mistral-large4"],"editorial":{"tldr":"This page groups items by the shared phrase \"large language models\", so most of it is a reading list, not one thread. Oct 16-27 items cover sycophancy, code-translation hallucinations, search agents, steering and continual learning.","stale":false,"whats_new":"Oct 29: Mistral Large 4 is released, with vLLM day-0 support (sparse MLA, speculative decoding, NIXL prefill/decode on Blackwell) and a Hugging Face launch post.","why_it_matters":"Mistral Large 4 is the one item here you can deploy: serving support landed the same day as the weights, so self-hosting is testable now. The papers remain a reading list.","take_for_builders":"If you self-host, pull Mistral Large 4 on the vLLM day-0 build and rerun your own evals and latency tests before swapping it in. Treat the Oct 22 price comparison as unverified until you check provider pricing pages."}},{"slug":"token-efficiency","label":"Token Efficiency","item_count":3,"day_count":3,"source_count":3,"first_seen":"2026-10-14T09:00:00+00:00","last_updated":"2026-10-28T23:38:44+00:00","latest_title":"A Lisp coding agent is beating Codex in token efficiency by 10x","days":["2026-10-14","2026-10-23","2026-10-28"],"member_sids":["72f3ee5066aaa64d","ac57fbd36999bb7e","9f96f9f3a384b51e"],"member_urls":["https://www.infoq.com/news/2026/10/antigravity-sdk-local-models/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=AI%2C+ML+%26+Data+Engineering","http://arxiv.org/abs/2610.32813v1","https://twitter.com/techfren/status/2115583260919202187"],"editorial":{"tldr":"Google's Antigravity SDK added local-model support on Oct 14, pitched as a token-efficiency and code-privacy gain. A paper on Oct 23 then targeted the harness itself, where reasoning is re-invoked after tool feedback even when the next action is already fixed.","stale":false,"whats_new":"A Hacker News post on Oct 28 claims a Lisp coding agent beats Codex by 10x on token efficiency; the claim is unverified and the linked source is a social post.","why_it_matters":"Agent token spend is shifting from model choice to harness design: how often the loop re-invokes reasoning and whether work runs locally.","take_for_builders":"Log tokens per completed task in your own harness and test whether skipping a reasoning call after tool output changes success rate before trusting any vendor or social-media multiplier.","status":{"state":"Developing","tone":"rising","changed":"2026-10-28","detail":"Three loosely related items; only one is a peer-style method, and the latest 10x claim lacks published methodology in the bundle.","track":[{"label":"local models","detail":"Oct 14","tone":"launch","weight":30},{"label":"harness method","detail":"Oct 23","tone":"rising","weight":40},{"label":"10x claim","detail":"Oct 28","tone":"now","weight":30}]}}},{"slug":"continual-learning","label":"Continual Learning","item_count":4,"day_count":4,"source_count":3,"first_seen":"2026-10-22T15:03:20+00:00","last_updated":"2026-10-28T20:03:05+00:00","latest_title":"Routed Continual Learning","days":["2026-10-22","2026-10-25","2026-10-27","2026-10-28"],"member_sids":["aed209b05e4ae391","55edc2615764e1b5","200271de2a2f52aa","7bae6548f927e204"],"member_urls":["http://arxiv.org/abs/2610.31401v1","http://arxiv.org/abs/2610.33956v1","http://arxiv.org/abs/2610.36734v1","http://arxiv.org/abs/2610.38796v1"],"editorial":{"tldr":"An Oct 22 paper (HERD) framed continual learning as a trade-off between parametric methods, which forget, and non-parametric ones. A benchmark followed on Oct 25 for whether LLM agents improve from experience, then CAMR on Oct 27 for choosing what to replay.","stale":false,"whats_new":"Oct 28: Routed Continual Learning lets deployed agents share experience, routing among retention mechanisms rather than relying on a single one.","why_it_matters":"Agents that learn from experience need retention without regressions; replay selection, long-horizon evals and now cross-agent sharing are the pieces to watch.","take_for_builders":"Treat these as research signals: do not adopt, but add a retention check to any fine-tune or agent-memory pipeline that updates on new data.","status":{"state":"Developing · research","tone":"rising","changed":"2026-10-28","detail":"Four arXiv papers in seven days; all research-stage, none shipped in a product."}}},{"slug":"machine-learning","label":"Machine Learning","item_count":4,"day_count":3,"source_count":4,"first_seen":"2026-10-21T15:38:05+00:00","last_updated":"2026-10-28T20:00:47+00:00","latest_title":"Trustworthy Distributed Machine Learning in Cloud-Edge-IoT Systems: A Heterogeneity-Aware Survey of Security, Privacy, and Robustness","days":["2026-10-21","2026-10-23","2026-10-28"],"member_sids":["16071b3c73317004","94a5f6526eada9bc","cdd1bfaa5cdbcda5","cd86ac953a59e37c"],"member_urls":["http://arxiv.org/abs/2610.29895v1","https://huggingface.co/blog/nvidia/kumo-relational","http://arxiv.org/abs/2610.32914v1","http://arxiv.org/abs/2610.38793v1"],"editorial":{"tldr":"On Oct 21, a paper on extracting materials data from literature with ML and LLMs appeared alongside NVIDIA's Kumo Relational, which claims to beat supervised ML on relational data with no training. A physics-constrained ML tutorial followed on Oct 23.","stale":false,"whats_new":"A survey (Oct 28) maps security, privacy and robustness risks of distributed ML and federated learning across cloud-edge-IoT systems, with a focus on heterogeneity.","why_it_matters":"Earlier items cut task-specific training or encode first-principles constraints; the survey covers what breaks when training is spread across heterogeneous devices. All four are research items, not shipped tooling.","take_for_builders":"Before adopting Kumo Relational, rerun it against your own supervised baseline on your relational data; if you train across edge devices, use the survey's threat categories as a review checklist."}},{"slug":"policy-optimization","label":"Policy Optimization","item_count":7,"day_count":6,"source_count":4,"first_seen":"2026-10-14T15:27:50+00:00","last_updated":"2026-10-28T10:38:43+00:00","latest_title":"How Deterministic SAST and AI Security Agents Divide the Work: Coverage, Harness Policy and Cost in Snyk VulnBench 2.0","days":["2026-10-14","2026-10-22","2026-10-23","2026-10-26","2026-10-27","2026-10-28"],"member_sids":["a42e86ea9f9f98f3","9cbcaf295333969d","78e0961a0062764b","500b1aba48043738","6f21f6e8dedb00a0","9c6961c015612e8b","d24c53b475096517"],"member_urls":["http://arxiv.org/abs/2610.20350v1","http://arxiv.org/abs/2610.30933v1","http://arxiv.org/abs/2610.32212v1","http://arxiv.org/abs/2610.35013v1","http://arxiv.org/abs/2610.35832v1","http://arxiv.org/abs/2610.37255v1","http://arxiv.org/abs/2610.38617v1"],"editorial":{"tldr":"On Oct 14 a paper proposed Lifted State Policy Optimization to credit the local change that turns a flawed reasoning state into a better one. ReflectPO (Oct 22) and SOPO (Oct 23) then applied policy optimization to long-horizon agents and hallucination detection.","stale":false,"whats_new":"Snyk VulnBench 2.0 (Oct 28) compares deterministic SAST with AI security agents on 108 hand-curated cases, reporting coverage, harness policy and cost, after papers framing the policy as part of the harness.","why_it_matters":"The thread shifts from training the policy to the interface around it: the harness and policy together decide agent behavior, so harness changes need their own evals.","take_for_builders":"Treat these as preprints: before changing a training pipeline, wait for code and replication; meanwhile, rerun your agent evals whenever the harness changes, not only the model."}},{"slug":"benchmarking-long-horizon","label":"Benchmarking Long-Horizon","item_count":7,"day_count":6,"source_count":4,"first_seen":"2026-10-14T15:24:39+00:00","last_updated":"2026-10-28T10:06:57+00:00","latest_title":"CALM: Conflict-Aware Lifecycle Memory Governance for Long-Horizon Language Agents","days":["2026-10-14","2026-10-19","2026-10-20","2026-10-25","2026-10-26","2026-10-28"],"member_sids":["9d061413e7a1baa2","736cb3afaf8cf4b3","0cabd28beabc87ef","55edc2615764e1b5","5f97c21b490b1ce9","3ca73f33ea96bd6a","423c688c0621c177"],"member_urls":["http://arxiv.org/abs/2610.20344v1","http://arxiv.org/abs/2610.25980v1","http://arxiv.org/abs/2610.27645v1","http://arxiv.org/abs/2610.33956v1","http://arxiv.org/abs/2610.35150v1","http://arxiv.org/abs/2610.35801v1","http://arxiv.org/abs/2610.38573v1"],"editorial":{"tldr":"Doc2Harness opened the thread on Oct 14 by measuring how much the harness around a model, not just the model, drives long-horizon coding-agent results. Papers then added search-state learning (ClueFlow), error-recovery tests for computer-use agents (CUA-Recovery) and a continual-learning exam.","stale":false,"whats_new":"CALM (Oct 28) proposes conflict-aware lifecycle memory governance, so agents can retire outdated memories without losing the ability to reconstruct the past.","why_it_matters":"Long-horizon agent failures come from harness design, silent state corruption, weak credit assignment and stale memory, so benchmarks that isolate those factors beat single-score leaderboards for platform decisions.","take_for_builders":"When evaluating a long-horizon agent, rerun it under your own harness and add an injected-error recovery case before trusting a leaderboard score; audit how your agent expires stale memory.","status":{"state":"Developing","tone":"rising","changed":"2026-10-28","detail":"Seven arXiv preprints, no independent replication yet; benchmarks, training methods and memory management are arriving together."}}}]}