AI can do so many things now, no time to waste. PhD in CS.
Joined July 2016
- Tweets6.2K
- Following2.8K
- Followers3.6K
- Likes18.2K
nle retweeted
Muse Spark 1.3 is rolling out today with frontier performance almost too cheap to meter. This is the biggest jump we've made so far on coding and agentic work. Try it in Muse Code and our API.
Next up 🍉 and Muse Spark open weights releases coming soon.
nle retweeted
Introducing Gemini 3.8 Flash, another jump in Gemini's agentic + coding capabilities, and our 3rd updated Flash model in only 6 weeks...
This model has been a ton of fun to work with, excited to see what you all think!
nle retweeted
Big news: Qwen3.8-Max-0902 by @Alibaba_Qwen just debuted at #1 overall in the Code Arena: WebDev with 1691 pts!
It scores 3 pts above Claude Opus 5 (Max), 17 pts above Kimi K3 (Max), and 22 pts above the previous Qwen3.8-Max.
Priced at a blended $5/MToken, Qwen3.8-Max-0902 also claims the highest-scoring position on the Pareto frontier! Stay tuned for a closer look at its Pareto positioning, and for Agent Arena scores coming soon.
Its strength carries across every Code Arena: WebDev category:
- #1 in Data & Analytics and Consumer Product
- #2 in Brand & Marketing, Gaming, and Simulations
- #3 in Content Creation Tools and Reference-Based Design
Congrats to the @Alibaba_Qwen team on this huge update!
🚀Qwen3.8-Max just got upgraded. Meet Qwen3.8-Max-0902!
2.4T parameters. 1M context tokens. Built for real world complexity.
Further post trained on Coding & Cowork, Qwen3.8-Max-0902 now delivers stronger performance across complex enterprise tasks, scientific research, and long horizon workflows.
💰Pricing per 1M tokens:
$2 input, $6 output.
$0.17 explicit cache hit, $0.25 implicit cache hit.
Now live via API on QwenCloud. Come try it! 🙌
API: qwencloud.com/models/qwen3.8…
nle retweeted
Holy mother of God they cooked on Astra..
"We now believe Astra meets the Critical cybersecurity capability threshold under our Preparedness Framework"
"We plan to make Astra available soon, but access to its most advanced cybersecurity capabilities will be more limited."
“We are also deploying misalignment monitoring in production for Astra-class models in order to detect and rapidly contain potential misalignment”
We knew internally they were ramping up monitoring late July/August - glad to see the fruits of that.
12.5x is nice but not yet competitive to the market
- OpenAI Plus 27x, Pro $200 54x headline API price + Tibo
- SuperGrok 38x, especially Grok 4.6 pricing is kind of similar to GLM 5.3
If they could make it go to the zone of 25-30x then they will attract a lot more users.
For everyone asking about GLM-5.3 quota on the latest GLM Coding Plan: the same budget can cover up to 12.5x pay-as-you-go usage when used entirely off-peak. That’s up to 92% savings.
Peak hours are weekdays, 11 PM–3 AM PT.
docs.z.ai/devpack/overview#e…
2
158
nle retweeted
The whole “20x” in Claude’s pricing is so misleading.
The $100 plan says 5x Pro limits, while the $200 plan says 20x Pro limits. That makes it sound like you’re getting 4x the usage.
But the 20x applies only to the 5-hour usage window. The weekly limit is basically just 2x the $100 plan.
I spent way too much time digging through the internet and Reddit to figure this out. They could’ve just stated the limits directly instead of marketing it as “20x.”
That alone would’ve saved me hours of confusion.
nle retweeted
⚡Meet Qwen3.8-Flash, a multimodal MoE and an early preview of the Qwen4 architecture, now open-weight!
The production version Qwen3.8-Flash will be available soon via QwenCloud API at just $ 0.16/1M input tokens and $ 0.47/1M output tokens.
125B parameters + 51B N-gram embeddings, with just 6B activated per token. Unmatched cost-efficiency.
What's new: 🥳
- Next architecture: GDN + QSA hybrid attention, Gated Residual, N-gram Embedding & Muon optimizer, serving as a precursor to the architecture used in Qwen4.
- Dramatically lower training and inference costs: trained at just 1/9 the cost of Qwen3.7-Plus, while outperforming it across the board with especially strong gains in coding and office tasks.
- Strong performance: scoring 58.7 on DeepSWE 1.1, 62.5 on SWE-bench Pro, 73.9 on CoWorkBench, 84.5 on AndroidWorld, and 95.7 on MathVision (with CI).
- 262K native context, extensible to 1M with YaRN.
We’re also releasing the weights for Qwen3.8-Flash-Next, giving the community an early look at the new architecture we’re exploring for Qwen4.🚀
We can't wait to see what you build with Qwen3.8-Flash!👀👇
- Blog: qwen.ai/blog?id=qwen3.8-flas…
- Technical Report: github.com/QwenLM/Qwen3.8-Fl…
- Hugging Face: huggingface.co/Qwen/Qwen3.8-…
- ModelScope: modelscope.cn/models/Qwen/Qw…
Qwen 3.8 27B + Opencode works surprisingly well for medium-diff tasks. I feel confident that I can leave it unattended running and it gonna deliver without messing up.
That kind of confidence I only feel at gpt-5.2+.
2
194
nle retweeted
Qwen3.8-27B can now be run locally! ✨
Run on 17GB RAM via Unsloth Dynamic GGUFs.
Qwen3.8-27B is by far the strongest model for its size. We also uploaded NVFP4 quants.
GGUF: huggingface.co/unsloth/Qwen3…
Guide: unsloth.ai/docs/models/qwen3…
We promised open weights for Qwen3.8. Now, time to meet them! 🎉
⚡ Qwen3.8-27B:
- A native multimodal dense model. With just 27B parameters, it outperforms Qwen3.7-Plus overall and shines in real-world coding & office workflows.
- 262K native context, easily extendable to 1M tokens via YaRN.
- Built for builders. Highly efficient, high-quality, and licensed under Apache 2.0.
🚀 The open weights for Qwen3.8-2.4T-A95B (Max-level) have also been released recently.
Whether you're shipping lightweight applications with Qwen3.8-27B locally or building agents with Qwen3.8-2.4T-A95B, they're yours now!
Download, deploy, and build something we haven't imagined yet. 👀👇
- Hugging Face：
huggingface.co/collections/Q…
- ModelScope：
modelscope.cn/collections/Qw…
nle retweeted
Qwen-3.8 27b released! Significant jump!
Qwen just released Qwen3.8-27B, a compact open-weight multimodal model that reportedly competes with much larger frontier systems.
Despite having only 27B parameters, it claims state-of-the-art results in agentic coding, computer use, browser tasks, and long-horizon professional work, beating Claude Opus 4.6 Max on several benchmarks. It supports images, video, configurable reasoning, a native 262K context window expandable to 1M, and can be self-hosted under Apache 2.0.
tl;dr: frontier-level agent capabilities are becoming smaller, cheaper, and openly deployable. The benchmark results are Qwen’s own and still need independent verification.
nle retweeted
Introducing GLM-5.3: Built to Code. Ready for Cyber Defense.
- Top-tier coding and agentic capabilities, achieved through post-training on the 743B base model
- A major leap in cybersecurity, setting a new standard among open models
Tech Blog: z.ai/blog/glm-5.3
It is nice to have but that is it. Long running agents involve a lot of i/o websearch test runs... 14x speed will only give probaby 2x wall clock.
Previewing Ultrafast mode: GPT-5.6 Sol at up to 14x the speed.
Launching first in the OpenAI API to a select group of customers with expanded access to more businesses as capacity grows.
147
nle retweeted
Introducing Gemini 3.7 Flash : )
- it is fast!
- 50% lower price than 3.6 flash (through end of year)
- strong intelligence increase in only ~3 weeks (thanks to some awesome algorithmic improvements)
- available in the API, AI Studio, Antigravity, and more!
nle retweeted
We’re launching DeepSeek-V4-Pro today! 🚀
🔷 Major Agent upgrades with strong production gains!
🔷 Flexible reasoning effort for V4-Pro & V4-Flash: low for simple tasks, high for daily Agent workflows, max for complex tasks.
🔷 Native OpenAI Responses API support, optimized for Codex with one-click setup.
V4 Pro is now available on app/web. Try it via “Expert Mode”.
V4 Pro is also available via API. Model names remain unchanged—please refer to the API docs for setup details.
nle retweeted
Introducing Grok 4.6.
It delivers frontier intelligence and is a significant improvement over Grok 4.5 at the same price.
nle retweeted
Today we're also opening the weights for Muse Glimmer, a great 30B parameter dense model that can run locally. Soon we'll also release the weights for Muse Spark 1.2, our latest foundation model. Meta is a strong supporter of open source and I'm proud of these releases. Congrats to @alexandr_wang and the MSL team for all your great work on these models.