5 March 2026
Ai Top News
A “ChatGPT for spreadsheets” helps solve difficult engineering challenges faster
Tl;dr: MIT researchers have developed an approach that accelerates Bayesian optimization for high-dimensional problems by leveraging a tabular foundation model, achieving significant speedups over existing methods.
Key Takeaways:
- A team of MIT researchers used a tabular foundation model to accelerate Bayesian optimization for high-dimensional problems, achieving 10 to 100 times faster results compared to existing methods.
- The approach identifies the most critical design features for improvement and focuses the search on those variables, making it well-suited for complex engineering problems like power grid design and car safety.
- The researchers' approach leverages the efficiency of a pre-trained tabular foundation model to adapt to different applications without constant retraining, enhancing the optimization process.
Yesterday
Raycast’s Glaze is an all-in-one vibe coding app platform
Tl;dr: Raycast has launched Glaze, a new AI-powered coding platform for Mac that lets users build and share applications without writing code.
Key Takeaways:
- Raycast has introduced Glaze, an AI-powered coding platform for Mac that simplifies the process of building and sharing applications.
- Glaze uses underlying models from Claude Code and OpenAI's Codex to create applications in one go, with minimal user intervention.
- The platform is available for Mac now and will be expanded to Windows and mobile devices in the future.
- Glaze is integrated with Raycast, allowing users to launch and manage applications built on the platform.
17h ago
News Corp is essentially an AI ‘input company’, chief executive says, after US$150m deal with Meta - The Guardian
Tl;dr: News Corp has signed a deal with Meta valued at up to US$50m per year, which will allow Facebook and Instagram to scrape the media company's content to train AI products.
Key Takeaways:
- News Corp has signed a deal with Meta to license its content for AI training, making the media company an AI 'input' provider.
- The deal, valued at up to US$50m per year, will see Meta scrape content from News Corp's US and UK publications.
- News Corp's global CEO, Robert Thomson, described his company as an 'input company' similar to semiconductor providers or data centres.
Yesterday
Open Source
Fireflies and Otter just launched MCP connectors for meeting data — here's the open-source one you can self-host
Fireflies just became the first meeting tool in Anthropic's official Claude MCP Directory. Otter.ai launched an enterprise MCP server too. tl;dv has one as well. The "meeting data + MCP" space is heating up fast.
But all three are closed-source, cloud-only. Your meeting data — strategy discussions, financials, personnel decisions — goes through their servers.
I've been building Vexa, an open-source meeting bot API, and we've had a **native MCP server** since before any of them. The difference: it's Apache 2.0, and you can run the entire stack on your own infrastructure.
**Setup** (takes \~2 minutes):
{
"mcpServers": {
"vexa": {
"url": "https://api.cloud.vexa.ai/mcp",
"headers": {"X-API-Key": "your-key"}
}
}
}
Drop that in your Claude Desktop config, and you can ask:
* "What did we decide about pricing in last Tuesday's meeting?"
* "Summarize action items from all meetings this week"
* "Find every time \[person\] mentioned the deadline"
**Or self-host the whole thing:**
git clone https://github.com/Vexa-ai/vexa
cd vexa
docker compose up
MCP server included. Your meeting data never leaves your network.
GitHub: [https://github.com/Vexa-ai/vexa](https://github.com/Vexa-ai/vexa) (1,700+ stars, Apache 2.0)
Happy to answer questions about MCP, the architecture, or how this compares to Fireflies/Otter's approach.
--- TOP COMMENTS ---
>
---
mcp for meetings is a good fit. can you query across workspaces or per-workspace only?..
But all three are closed-source, cloud-only. Your meeting data — strategy discussions, financials, personnel decisions — goes through their servers.
I've been building Vexa, an open-source meeting bot API, and we've had a **native MCP server** since before any of them. The difference: it's Apache 2.0, and you can run the entire stack on your own infrastructure.
**Setup** (takes \~2 minutes):
{
"mcpServers": {
"vexa": {
"url": "https://api.cloud.vexa.ai/mcp",
"headers": {"X-API-Key": "your-key"}
}
}
}
Drop that in your Claude Desktop config, and you can ask:
* "What did we decide about pricing in last Tuesday's meeting?"
* "Summarize action items from all meetings this week"
* "Find every time \[person\] mentioned the deadline"
**Or self-host the whole thing:**
git clone https://github.com/Vexa-ai/vexa
cd vexa
docker compose up
MCP server included. Your meeting data never leaves your network.
GitHub: [https://github.com/Vexa-ai/vexa](https://github.com/Vexa-ai/vexa) (1,700+ stars, Apache 2.0)
Happy to answer questions about MCP, the architecture, or how this compares to Fireflies/Otter's approach.
--- TOP COMMENTS ---
>
---
mcp for meetings is a good fit. can you query across workspaces or per-workspace only?..
15h ago
Qwen3 9B can run fine on android phones at q4_0
tried it earlier on an s25 ultra with 12 gigs of ram and snapdragon 8 elite chip, got a >6 tokens/s generation speed.
used the hexagon npu option for the test
--- TOP COMMENTS ---
I wish they had a 8b-1b active MOE in the qwen 3.5. These models are nice in that they can run on my phone but they're so slow.
---
In 5 years qwen will run on a toaster ..
used the hexagon npu option for the test
--- TOP COMMENTS ---
I wish they had a 8b-1b active MOE in the qwen 3.5. These models are nice in that they can run on my phone but they're so slow.
---
In 5 years qwen will run on a toaster ..
10h ago
Qwen3.5-35B-A3B hits 37.8% on SWE-bench Verified Hard — nearly matching Claude Opus 4.6 (40%) with the right verification strategy
[Qwen3.5-35B-A3B hits 37.8% on SWE-bench Verified Hard](https://preview.redd.it/ecvh8rwhxymg1.png?width=2081&format=png&auto=webp&s=ac79a8173c4b0f781749d23f404c1d73e989009a)
[cumulative resolution vs steps](https://preview.redd.it/f31egqjkxymg1.png?width=1773&format=png&auto=webp&s=41ee70bec949634a2f162a376f1f1532c3b8fe39)
I've been running experiments on SWE-bench Verified with a tiny MoE model (Qwen3.5-35B-A3B, only 3B active params) self-hosted via vLLM, and the results surprised me.
TL;DR: By adding a simple "verify after every edit" nudge to the agent loop, a 3B-active model goes from 22% → 38% on the hardest SWE-bench tasks, nearly matching Claude Opus 4.6's 40%. On the full 500-task benchmark, it hits 67.0% — which would put it in the ballpark of much larger systems on the official leaderboard.
**What I tried**
I build a minimal agent harness (tools : `file_read`, `file_edit`, `bash`, `grep` , `glob`) and iterated on verification strategies :
|Strategy|Hard (45 tasks)|Full (500 tasks)|
|:-|:-|:-|
|agent-harness (baseline, no self-verification)|22.2%|64%|
|verify-at-last (write test script before declaring done)|33.3%|67%|
|verify-on-edit (force agent to test after every `file_edit`)|37.8%|\-|
|Claude Opus 4.6 (for reference) |40.0%||
The "verify-on-edit" strategy is dead simple — after every successful file\_edit, I inject a user message like:
"You just edited X. Before moving on, verify the change is correct: write a short inline python -c or a /tmp test script that exercises the changed code path, run it with bash, and confirm the output is as expected."
That's it. No fancy search algorithms, no reward models, no multi-agent setups. Just telling the model to check its work after every edit.
**what didn't work**
* MCTS / tree search: Tried multiple variants, all performed worse than the straight-line baseline. Verifier scores didn't correlate with actual resolution. Tree search breaks the coherent reasoning flow that small models need.
* \- Best-of-N sampling: Some marginal gains but not worth the compute.
**Code + configs + all experiment logs:** [**github.com/SeungyounShin/agent-verify**](http://github.com/SeungyounShin/agent-verify)
--- TOP COMMENTS ---
My suggestion would be to wait till swe-rebench has enough new tasks and re-run your evals. The problem with swebench is that it is old, and models released now can have leaked signals in their training data.
---
Sorry but this cannot be true. Clearly benchmaxed. Even the 3.5 397B deleted multiple files without asking in opencode yesterday...
[cumulative resolution vs steps](https://preview.redd.it/f31egqjkxymg1.png?width=1773&format=png&auto=webp&s=41ee70bec949634a2f162a376f1f1532c3b8fe39)
I've been running experiments on SWE-bench Verified with a tiny MoE model (Qwen3.5-35B-A3B, only 3B active params) self-hosted via vLLM, and the results surprised me.
TL;DR: By adding a simple "verify after every edit" nudge to the agent loop, a 3B-active model goes from 22% → 38% on the hardest SWE-bench tasks, nearly matching Claude Opus 4.6's 40%. On the full 500-task benchmark, it hits 67.0% — which would put it in the ballpark of much larger systems on the official leaderboard.
**What I tried**
I build a minimal agent harness (tools : `file_read`, `file_edit`, `bash`, `grep` , `glob`) and iterated on verification strategies :
|Strategy|Hard (45 tasks)|Full (500 tasks)|
|:-|:-|:-|
|agent-harness (baseline, no self-verification)|22.2%|64%|
|verify-at-last (write test script before declaring done)|33.3%|67%|
|verify-on-edit (force agent to test after every `file_edit`)|37.8%|\-|
|Claude Opus 4.6 (for reference) |40.0%||
The "verify-on-edit" strategy is dead simple — after every successful file\_edit, I inject a user message like:
"You just edited X. Before moving on, verify the change is correct: write a short inline python -c or a /tmp test script that exercises the changed code path, run it with bash, and confirm the output is as expected."
That's it. No fancy search algorithms, no reward models, no multi-agent setups. Just telling the model to check its work after every edit.
**what didn't work**
* MCTS / tree search: Tried multiple variants, all performed worse than the straight-line baseline. Verifier scores didn't correlate with actual resolution. Tree search breaks the coherent reasoning flow that small models need.
* \- Best-of-N sampling: Some marginal gains but not worth the compute.
**Code + configs + all experiment logs:** [**github.com/SeungyounShin/agent-verify**](http://github.com/SeungyounShin/agent-verify)
--- TOP COMMENTS ---
My suggestion would be to wait till swe-rebench has enough new tasks and re-run your evals. The problem with swebench is that it is old, and models released now can have leaked signals in their training data.
---
Sorry but this cannot be true. Clearly benchmaxed. Even the 3.5 397B deleted multiple files without asking in opencode yesterday...
23h ago
Qwen3.5-0.8B - Who needs GPUs?
I am genuinely surprised at how good the model is and that it can run on 14 years old device: 2nd gen i5 + 4GB DDR3 RAM.
--- TOP COMMENTS ---
I bet it's as good as GPT-3. Just remember how amazed we were few years ago, and now we have the same model but open source and can be run on a potato.
---
semi-transparent terminals are still in fashion? I remember enlightenment and compiz like 20 years ago ;)..
--- TOP COMMENTS ---
I bet it's as good as GPT-3. Just remember how amazed we were few years ago, and now we have the same model but open source and can be run on a potato.
---
semi-transparent terminals are still in fashion? I remember enlightenment and compiz like 20 years ago ;)..
17h ago
Qwen3.5-27B Q4 Quantization Comparison
This is a Q4 quantization sweep across all major community gguf quants of Qwen3.5-27B (available the 03/03/2026), comparing mean KLD to the BF16 baseline across different quantizers and recipes.
The goal is to give people a data-driven basis for picking a file rather than just grabbing whatever is available.
KLD (KL Divergence): "Faithfulness." It shows how much the quantized model's probability distribution drifts from the probability distribution of the original weights. Lower = closer.
# KLD Results — Custom Chat Dataset
Evaluated on `titwitMuffbiscuit-v03-full.txt` — chat-wrapped corpus (Qwen3.5 ChatML format), 47 chunks -c 4096. Content: Science & engineering, Medicine, Philosophy, History, Finance, Culture, multilingual content and code snippets.
[lmstudio-community and mradermacher standard Q4\_K\_M are identical — stacking on the plot.](https://preview.redd.it/kf39ily54xmg1.png?width=2979&format=png&auto=webp&s=00a054c35288ad2f62e4f0ecb1d406787a7d0a42)
# Wikitext2 + Custom Dataset Comparison
Evaluated on `wikitext2_test.txt`, 72 chunks -c 4096. Content: plain text english.
The dumbbell plot shows both datasets side by side.
[lmstudio-community and mradermacher standard Q4\_K\_M are identical — blending visible on the dumbbell plot.](https://preview.redd.it/o7xdrxt74xmg1.png?width=2979&format=png&auto=webp&s=e78996249dea09f8647141c1fc52f547678ff066)
# Sorted by KLD — Custom Dataset
|Rank|Quantization|Size (GiB)|PPL|KLD|
|:-|:-|:-|:-|:-|
|1|unsloth\_Qwen3.5-27B-UD-Q4\_K\_XL|16.411|5.8901|0.005087|
|2|bartowski\_Qwen3.5-27B-Q4\_K\_M|15.952|5.8882|0.005633|
|3|unsloth\_Qwen3.5-27B-Q4\_K\_M|15.591|5.8948|0.006193|
|4|ubergarm\_Qwen3.5-27B-smol-IQ4\_NL|15.415|5.9026|0.006371|
|5|mradermacher\_Qwen3.5-27B.i1-Q4\_K\_M|15.404|5.9059|0.006469|
|6|bartowski\_Qwen3.5-27B-Q4\_K\_S|14.985|5.8984|0.006720|
|7|bartowski\_Qwen3.5-27B-IQ4\_XS|14.130|5.9017|0.007062|
|8|bartowski\_Qwen3.5-27B-IQ4\_NL|14.851|5.9091|0.007233|
|9|unsloth\_Qwen3.5-27B-Q4\_K\_S|14.686|5.9083|0.007449|
|10|unsloth\_Qwen3.5-27B-IQ4\_NL|14.610|5.9147|0.007461|
|11|mradermacher\_Qwen3.5-27B.i1-IQ4\_XS|13.680|5.9129|0.007569|
|12|unsloth\_Qwen3.5-27B-IQ4\_XS|13.949|5.9179|0.007677|
|13|mradermacher\_Qwen3.5-27B.i1-Q4\_K\_S|14.499|5.9209|0.007937|
|14|mradermacher\_Qwen3.5-27B.Q4\_K\_M|15.404|5.9028|0.009201|
|15|mradermacher\_Qwen3.5-27B.IQ4\_XS|13.784|5.9342|0.011463|
|16|steampunque\_Qwen3.5-27B.Q4\_K\_H|14.864|5.9050|0.012091|
|17|mradermacher\_Qwen3.5-27B.Q4\_K\_S|14.499|5.9293|0.012364|
*lmstudio-community Q4\_K\_M excluded — identical file to mradermacher Q4\_K\_M.*
# Most Efficient Quantization — Custom Dataset
The Efficiency Score is the distance to a 'perfect' model (zero size, zero KLD), not the 'best' model but the VRAM sweet spot.
Efficiency Score: √ (Normalized Size² + Normalized KLD²) — lower is better.
|Rank|Quantization|Size (GiB)|KLD|Eff. Score|
|:-|:-|:-|:-|:-|
|1|bartowski\_Qwen3.5-27B-IQ4\_XS|14.130|0.007062|0.317506|
|2|mradermacher\_Qwen3.5-27B.i1-IQ4\_XS|13.680|0.007569|0.341075|
|3|unsloth\_Qwen3.5-27B-IQ4\_XS|13.949|0.007677|0.369294|
|4|unsloth\_Qwen3.5-27B-IQ4\_NL|14.610|0.007461|0.471585|
|5|unsloth\_Qwen3.5-27B-Q4\_K\_S|14.686|0.007449|0.490965|
|6|mradermacher\_Qwen3.5-27B.i1-Q4\_K\_S|14.499|0.007937|0.493275|
|7|bartowski\_Qwen3.5-27B-IQ4\_NL|14.851|0.007233|0.520404|
|8|bartowski\_Qwen3.5-27B-Q4\_K\_S|14.985|0.006720|0.527916|
|9|mradermacher\_Qwen3.5-27B.i1-Q4\_K\_M|15.404|0.006469|0.659219|
|10|ubergarm\_Qwen3.5-27B-smol-IQ4\_NL|15.415|0.006371|0.659346|
|11|unsloth\_Qwen3.5-27B-Q4\_K\_M|15.591|0.006193|0.716059|
|12|bartowski\_Qwen3.5-27B-Q4\_K\_M|15.952|0.005633|0.835306|
|13|mradermacher\_Qwen3.5-27B.Q4\_K\_M|15.404|0.009201|0.847417|
|14|mradermacher\_Qwen3.5-27B.IQ4\_XS|13.784|0.011463|0.877012|
|15|unsloth\_Qwen3.5-27B-UD-Q4\_K\_XL|16.411|0.005087|1.000000|
|16|mradermacher\_Qwen3.5-27B.Q4\_K\_S|14.499|0.012364|1.043999|
|17|steampunque\_Qwen3.5-27B.Q4\_K\_H|14.864|0.012091|1.055620|
**Hardware:** i3-12100F — 64GB DDR4-3200 — RTX 3060 12GB
**Evaluation tool:** llama.cpp (mainline) version: 8189 (4d828bd1a)
Notes:
Those results have been taken after the latest wave of quant update but lmstudio have yet to fix them.
I haven't included DevQuasar since not only they haven't updated them but one of their quant is mxfp4 (which results in a Q8\_0 when it's not an MoE).
I haven't included dinerburger either since the quant is relatively massive (IQ4\_NL at 20.2gb, bigger than Q5\_K\_M).
--- TOP COMMENTS ---
This is excellent. In a sea of different options, this truly helps!
---
Thanks for the work! How come for models like bartowski\_Qwen3.5-27B-IQ4\_XS you show a 14.1GB size when huggingface shows 15.2? ..
The goal is to give people a data-driven basis for picking a file rather than just grabbing whatever is available.
KLD (KL Divergence): "Faithfulness." It shows how much the quantized model's probability distribution drifts from the probability distribution of the original weights. Lower = closer.
# KLD Results — Custom Chat Dataset
Evaluated on `titwitMuffbiscuit-v03-full.txt` — chat-wrapped corpus (Qwen3.5 ChatML format), 47 chunks -c 4096. Content: Science & engineering, Medicine, Philosophy, History, Finance, Culture, multilingual content and code snippets.
[lmstudio-community and mradermacher standard Q4\_K\_M are identical — stacking on the plot.](https://preview.redd.it/kf39ily54xmg1.png?width=2979&format=png&auto=webp&s=00a054c35288ad2f62e4f0ecb1d406787a7d0a42)
# Wikitext2 + Custom Dataset Comparison
Evaluated on `wikitext2_test.txt`, 72 chunks -c 4096. Content: plain text english.
The dumbbell plot shows both datasets side by side.
[lmstudio-community and mradermacher standard Q4\_K\_M are identical — blending visible on the dumbbell plot.](https://preview.redd.it/o7xdrxt74xmg1.png?width=2979&format=png&auto=webp&s=e78996249dea09f8647141c1fc52f547678ff066)
# Sorted by KLD — Custom Dataset
|Rank|Quantization|Size (GiB)|PPL|KLD|
|:-|:-|:-|:-|:-|
|1|unsloth\_Qwen3.5-27B-UD-Q4\_K\_XL|16.411|5.8901|0.005087|
|2|bartowski\_Qwen3.5-27B-Q4\_K\_M|15.952|5.8882|0.005633|
|3|unsloth\_Qwen3.5-27B-Q4\_K\_M|15.591|5.8948|0.006193|
|4|ubergarm\_Qwen3.5-27B-smol-IQ4\_NL|15.415|5.9026|0.006371|
|5|mradermacher\_Qwen3.5-27B.i1-Q4\_K\_M|15.404|5.9059|0.006469|
|6|bartowski\_Qwen3.5-27B-Q4\_K\_S|14.985|5.8984|0.006720|
|7|bartowski\_Qwen3.5-27B-IQ4\_XS|14.130|5.9017|0.007062|
|8|bartowski\_Qwen3.5-27B-IQ4\_NL|14.851|5.9091|0.007233|
|9|unsloth\_Qwen3.5-27B-Q4\_K\_S|14.686|5.9083|0.007449|
|10|unsloth\_Qwen3.5-27B-IQ4\_NL|14.610|5.9147|0.007461|
|11|mradermacher\_Qwen3.5-27B.i1-IQ4\_XS|13.680|5.9129|0.007569|
|12|unsloth\_Qwen3.5-27B-IQ4\_XS|13.949|5.9179|0.007677|
|13|mradermacher\_Qwen3.5-27B.i1-Q4\_K\_S|14.499|5.9209|0.007937|
|14|mradermacher\_Qwen3.5-27B.Q4\_K\_M|15.404|5.9028|0.009201|
|15|mradermacher\_Qwen3.5-27B.IQ4\_XS|13.784|5.9342|0.011463|
|16|steampunque\_Qwen3.5-27B.Q4\_K\_H|14.864|5.9050|0.012091|
|17|mradermacher\_Qwen3.5-27B.Q4\_K\_S|14.499|5.9293|0.012364|
*lmstudio-community Q4\_K\_M excluded — identical file to mradermacher Q4\_K\_M.*
# Most Efficient Quantization — Custom Dataset
The Efficiency Score is the distance to a 'perfect' model (zero size, zero KLD), not the 'best' model but the VRAM sweet spot.
Efficiency Score: √ (Normalized Size² + Normalized KLD²) — lower is better.
|Rank|Quantization|Size (GiB)|KLD|Eff. Score|
|:-|:-|:-|:-|:-|
|1|bartowski\_Qwen3.5-27B-IQ4\_XS|14.130|0.007062|0.317506|
|2|mradermacher\_Qwen3.5-27B.i1-IQ4\_XS|13.680|0.007569|0.341075|
|3|unsloth\_Qwen3.5-27B-IQ4\_XS|13.949|0.007677|0.369294|
|4|unsloth\_Qwen3.5-27B-IQ4\_NL|14.610|0.007461|0.471585|
|5|unsloth\_Qwen3.5-27B-Q4\_K\_S|14.686|0.007449|0.490965|
|6|mradermacher\_Qwen3.5-27B.i1-Q4\_K\_S|14.499|0.007937|0.493275|
|7|bartowski\_Qwen3.5-27B-IQ4\_NL|14.851|0.007233|0.520404|
|8|bartowski\_Qwen3.5-27B-Q4\_K\_S|14.985|0.006720|0.527916|
|9|mradermacher\_Qwen3.5-27B.i1-Q4\_K\_M|15.404|0.006469|0.659219|
|10|ubergarm\_Qwen3.5-27B-smol-IQ4\_NL|15.415|0.006371|0.659346|
|11|unsloth\_Qwen3.5-27B-Q4\_K\_M|15.591|0.006193|0.716059|
|12|bartowski\_Qwen3.5-27B-Q4\_K\_M|15.952|0.005633|0.835306|
|13|mradermacher\_Qwen3.5-27B.Q4\_K\_M|15.404|0.009201|0.847417|
|14|mradermacher\_Qwen3.5-27B.IQ4\_XS|13.784|0.011463|0.877012|
|15|unsloth\_Qwen3.5-27B-UD-Q4\_K\_XL|16.411|0.005087|1.000000|
|16|mradermacher\_Qwen3.5-27B.Q4\_K\_S|14.499|0.012364|1.043999|
|17|steampunque\_Qwen3.5-27B.Q4\_K\_H|14.864|0.012091|1.055620|
**Hardware:** i3-12100F — 64GB DDR4-3200 — RTX 3060 12GB
**Evaluation tool:** llama.cpp (mainline) version: 8189 (4d828bd1a)
Notes:
Those results have been taken after the latest wave of quant update but lmstudio have yet to fix them.
I haven't included DevQuasar since not only they haven't updated them but one of their quant is mxfp4 (which results in a Q8\_0 when it's not an MoE).
I haven't included dinerburger either since the quant is relatively massive (IQ4\_NL at 20.2gb, bigger than Q5\_K\_M).
--- TOP COMMENTS ---
This is excellent. In a sea of different options, this truly helps!
---
Thanks for the work! How come for models like bartowski\_Qwen3.5-27B-IQ4\_XS you show a 14.1GB size when huggingface shows 15.2? ..
Yesterday
Hardware
Noble Machines, an 18 month old U.S. based company with a strong engineering team, deploys its first industrial humanoid built for the toughest and most dangerous jobs
Meet Noble Machines. 18 months from launch – shipped and deployed the first humanoid robot to a Fortune Global 500 industrial customer.
Founded by engineers from Apple, SpaceX, NASA, and Caltech – built on one conviction: AI must earn its place in the real world before it scales.
Focused on the toughest, most tiring, and most dangerous industrial tasks:
>27kg heavy load
>5-hour battery life
>Walking speed 0.8m/s
>Climbing stairs, traversing scaffolding, and navigating chaotic construction sites
> Modular end effector, allowing for quick tool change. > AI-controlled operation with end-to-end autonomy; learns new skills in hours
>Autonomous operation + Telep-op mode
>Rapid integration with existing enterprise workflows > Human-robot collaboration
https://www.noblemachines.ai
X.com/@UCR
--- TOP COMMENTS ---
the most expensive mule and can only carry up to 27kg?
---
https://preview.redd.it/37wvgnll51ng1.png?width=1483&format=png&auto=webp&s=e95c546c2cbefff5cc1cd3961f0f214bf4bcf592
..
Founded by engineers from Apple, SpaceX, NASA, and Caltech – built on one conviction: AI must earn its place in the real world before it scales.
Focused on the toughest, most tiring, and most dangerous industrial tasks:
>27kg heavy load
>5-hour battery life
>Walking speed 0.8m/s
>Climbing stairs, traversing scaffolding, and navigating chaotic construction sites
> Modular end effector, allowing for quick tool change. > AI-controlled operation with end-to-end autonomy; learns new skills in hours
>Autonomous operation + Telep-op mode
>Rapid integration with existing enterprise workflows > Human-robot collaboration
https://www.noblemachines.ai
X.com/@UCR
--- TOP COMMENTS ---
the most expensive mule and can only carry up to 27kg?
---
https://preview.redd.it/37wvgnll51ng1.png?width=1483&format=png&auto=webp&s=e95c546c2cbefff5cc1cd3961f0f214bf4bcf592
..
17h ago
[D] Intel Core Ultra 7 265K vs AMD Ryzen 7 7800X3D Which one is better for ML?
I am building a new PC for a mix of gaming and ML work, having a hard time to pick weather if I should go with Intel or AMD, current specs are 5070 ti, 32gb ram, what do u guys think?
Edit: Intel is the better choice here, there's barely any performance difference in terms of gaming
--- TOP COMMENTS ---
Intel Core Ultra 7 265K is generally the superior choice due to its higher core count, dedicated AI hardware, and significantly better multi-threaded performance.
There's about a 120% diff between the two for multi threaded performance
---
Intel is king for workloads ..
Edit: Intel is the better choice here, there's barely any performance difference in terms of gaming
--- TOP COMMENTS ---
Intel Core Ultra 7 265K is generally the superior choice due to its higher core count, dedicated AI hardware, and significantly better multi-threaded performance.
There's about a 120% diff between the two for multi threaded performance
---
Intel is king for workloads ..
8h ago
Applications
A “ChatGPT for spreadsheets” helps solve difficult engineering challenges faster
Tl;dr: MIT researchers have developed an approach that accelerates Bayesian optimization for high-dimensional problems by leveraging a tabular foundation model, achieving significant speedups over existing methods.
Key Takeaways:
- A team of MIT researchers used a tabular foundation model to accelerate Bayesian optimization for high-dimensional problems, achieving 10 to 100 times faster results compared to existing methods.
- The approach identifies the most critical design features for improvement and focuses the search on those variables, making it well-suited for complex engineering problems like power grid design and car safety.
- The researchers' approach leverages the efficiency of a pre-trained tabular foundation model to adapt to different applications without constant retraining, enhancing the optimization process.
Yesterday
I had Opus 4.6 evaluate 547 Reddit investing recommendations on reasoning quality with no upvote counts, no popularity signals. Its filtered picks returned +37% vs the S&P's +19%.
Hi everyone,
A couple weeks back, I ran an experiment where[ I fed 48 years of Buffett's shareholder letters to Claude Opus 4.6](https://www.reddit.com/r/ClaudeAI/comments/1rhbhoq/i_fed_opus_46_all_48_of_warren_buffetts/) and had it pick stocks blind (it matched 6 out of 10 Berkshire holdings without knowing what it was looking at). That experiment got a lot of great feedback and one of the most common requests was to test AI on real Reddit stock advice instead of just Buffett's principles.
I used Claude Code to build a multi-agent pipeline that grabs investing recommendations from r/ValueInvesting subreddit for the month of Feb 2025, strip popularity signals, and have Claude sub-agents score each investing recommendation blind on reasoning quality alone. Then I built three portfolios (10 stocks per portfolio):
* **The Crowd**: top 10 stocks ranked by total upvotes across all mentions
* **Claude's Picks**: top 10 stocks ranked by reasoning quality score
* **The Underdogs**: bottom 10 stocks by upvotes (min 5 upvotes), to test whether the crowd was right to ignore them
I tracked their real returns over a year from Feb 2025 - Feb 2026.
The part I found most interesting was that on data completely outside Opus's training window (Sep 2025 onward), Claude's picks returned +5.2% vs the most upvoted stocks only -10.8% (S&P 2%).
If you prefer to watch the full experiment, I uploaded it to my channel:[ https://www.youtube.com/watch?v=tr-k9jMS\_Vc](https://www.youtube.com/watch?v=tr-k9jMS_Vc) (free).
**The Setup**
I used Claude Code to scrape every single post from [r/ValueInvesting](https://www.reddit.com/r/ValueInvesting/) for the month of February 2025 and filter down to posts and comments where someone was recommending, analyzing, or debating a specific stock. This gave me 1,100+ qualifying threads, 6,000+ comments, and 547 individual stock recommendations across 238 unique tickers.
I then had Opus score every single one on five dimensions: thesis clarity, risk acknowledgment, data quality, specificity, and original thinking.
From there I built the three portfolios of **The Crowd**, **Claude's Picks**, **The Underdogs**.
All portfolios were equal-weight, bought on March 3, 2025 (first trading day of March). They had the same entry, same exit, with no cherry-picking.
Following was my Claude Code setup:
reddit-stock-analysis/
├── orchestrator # Main controller - runs full pipeline per month
├── skills/
│ ├── scrape-subreddit # Pulls all posts + comments for a given month via Reddit API
│ ├── filter-recommendations # Identifies posts where someone recommends/analyzes a stock
│ ├── extract-tickers # Maps mentions → ticker symbols, deduplicates
│ ├── strip-popularity # Removes upvote counts, awards, author karma
│ ├── build-portfolios # Constructs Crowd (by upvotes) vs AI (by score) vs Underdog
│ └── track-returns # Looks up actual price returns for each portfolio
└── sub-agents/
└── (spawned per recommendation) # Blind scoring - no popularity signals, just the post text
├── thesis-clarity # Is there a structured argument for why this stock?
├── risk-acknowledgment # Does the post address what could go wrong?
├── data-quality # Real financials (P/E, margins, debt) or just vibes?
├── specificity # Concrete targets, timeframes, catalysts?
└── original-thinking # Independent analysis or echoing the crowd?
**The Blind Test (Sep 2025 – Feb 2026)**
Before I share the main backtest, I want to start with the result I think matters more.
One fair criticism that keeps coming up in these experiments is that the AI might have seen these stock prices during training. The model I used has a training cutoff of August 2025, so the February recommendations do fall within that window. Even though the AI was only scoring argument quality (not predicting prices), it could theoretically recognize which stocks were being discussed.
So I reran the entire experiment on September 2025 recommendations, which is completely outside the model's training data. It resulted in over 800 threads, 10,500 comments, 2,200 recommendations scored. This guaranteed that the model did not have any knowledge of the stock price movement during this time in its training data.
AI: +5.2%
S&P 500: +2.4%
Crowd: -10.8%
On data the AI couldn't have possibly seen, it still beat the market. The crowd portfolio went negative. I think this is the cleanest result from the experiment because there's no way to argue the AI was cheating.
**The Full Backtest (Feb 2025 – Feb 2026)**
Now here's the full year backtest on the February data:
The Crowd: +39.8% (+20.3% vs S&P)
AI's Picks: +37.0% (+17.5% vs S&P)
S&P 500: +19.5%
Underdogs: +10.4% (-9.1% vs S&P)
The crowd actually won by about 3 percentage points. Both beast the S&P. But when I looked at the individual stocks, the story got a lot more interesting. AI's portfolio had 9 out of 10 winners. The worst performer was OSCR at -12%.
Both portfolios ended up in a similar place but the crowd went from +39.8% to -10.8% across the two time periods which feels quite inconsistent while Opus-filtered recommendations managed to gain both times.
**What I took away from this**
I don't think the takeaway is necessarily that "Opus picks better stocks." It's more that Opus appears to be better at telling apart solid analysis from stuff that just sounds good. It might serve as a good tool to filter out advice posts here down to solid ones that do good due diligence. The most popular advice and the best-reasoned advice had almost nothing to do with each other.
If this was interesting to you the full walkthrough is here including all the data:[ https://www.youtube.com/watch?v=tr-k9jMS\_Vc](https://www.youtube.com/watch?v=tr-k9jMS_Vc) (free)
Thank you so much if you did end up reading this far. Would love to hear if you have been experimenting similarly with Claude, let me know :-).
--- TOP COMMENTS ---
Have you calculated the statistical significance of the result?
What does the distribution of outcomes look like for a random strategy?
---
Methodology:
∙ How were ties handled in the scoring? When multiple sub-agents scored the same recommendation, what was the aggregation method — average, weighted, majority?
∙ Did any single stock dominate either portfolio’s returns? With 10-stock equal-weight portfolios, one outlier (positive or negative) can tell most of the story.
∙ What happened to the Underdogs portfolio in the Sep 2025 blind test? That comparison feels like the missing piece.
On the scoring dimensions:
∙ Were the five dimensions weighted equally? “Original thinking” and “data quality” feel like they should carry more weight than “specificity” if the goal is finding genuinely good analysis.
∙ Did high-scoring posts cluster around any particular sectors, or was it spread across the market?
On replication:
∙ Has anyone tried running the same pipeline on a different subreddit (r/stocks, r/investing) to see if the gap between crowd and reasoning-quality picks holds?
∙ What does the score distribution look like? Were most recommendations clustered in the middle, or was there a clear separation between high and low scorers?
The question I’m most curious about:
∙ Of the posts that scored high on reasoning quality but got almost no upvotes — what did they have in common stylistically? My guess is they were longer, more hedged, and less exciting to read. That would really nail down why popularity and quality diverge.
What did the data actually show on that last one?..
A couple weeks back, I ran an experiment where[ I fed 48 years of Buffett's shareholder letters to Claude Opus 4.6](https://www.reddit.com/r/ClaudeAI/comments/1rhbhoq/i_fed_opus_46_all_48_of_warren_buffetts/) and had it pick stocks blind (it matched 6 out of 10 Berkshire holdings without knowing what it was looking at). That experiment got a lot of great feedback and one of the most common requests was to test AI on real Reddit stock advice instead of just Buffett's principles.
I used Claude Code to build a multi-agent pipeline that grabs investing recommendations from r/ValueInvesting subreddit for the month of Feb 2025, strip popularity signals, and have Claude sub-agents score each investing recommendation blind on reasoning quality alone. Then I built three portfolios (10 stocks per portfolio):
* **The Crowd**: top 10 stocks ranked by total upvotes across all mentions
* **Claude's Picks**: top 10 stocks ranked by reasoning quality score
* **The Underdogs**: bottom 10 stocks by upvotes (min 5 upvotes), to test whether the crowd was right to ignore them
I tracked their real returns over a year from Feb 2025 - Feb 2026.
The part I found most interesting was that on data completely outside Opus's training window (Sep 2025 onward), Claude's picks returned +5.2% vs the most upvoted stocks only -10.8% (S&P 2%).
If you prefer to watch the full experiment, I uploaded it to my channel:[ https://www.youtube.com/watch?v=tr-k9jMS\_Vc](https://www.youtube.com/watch?v=tr-k9jMS_Vc) (free).
**The Setup**
I used Claude Code to scrape every single post from [r/ValueInvesting](https://www.reddit.com/r/ValueInvesting/) for the month of February 2025 and filter down to posts and comments where someone was recommending, analyzing, or debating a specific stock. This gave me 1,100+ qualifying threads, 6,000+ comments, and 547 individual stock recommendations across 238 unique tickers.
I then had Opus score every single one on five dimensions: thesis clarity, risk acknowledgment, data quality, specificity, and original thinking.
From there I built the three portfolios of **The Crowd**, **Claude's Picks**, **The Underdogs**.
All portfolios were equal-weight, bought on March 3, 2025 (first trading day of March). They had the same entry, same exit, with no cherry-picking.
Following was my Claude Code setup:
reddit-stock-analysis/
├── orchestrator # Main controller - runs full pipeline per month
├── skills/
│ ├── scrape-subreddit # Pulls all posts + comments for a given month via Reddit API
│ ├── filter-recommendations # Identifies posts where someone recommends/analyzes a stock
│ ├── extract-tickers # Maps mentions → ticker symbols, deduplicates
│ ├── strip-popularity # Removes upvote counts, awards, author karma
│ ├── build-portfolios # Constructs Crowd (by upvotes) vs AI (by score) vs Underdog
│ └── track-returns # Looks up actual price returns for each portfolio
└── sub-agents/
└── (spawned per recommendation) # Blind scoring - no popularity signals, just the post text
├── thesis-clarity # Is there a structured argument for why this stock?
├── risk-acknowledgment # Does the post address what could go wrong?
├── data-quality # Real financials (P/E, margins, debt) or just vibes?
├── specificity # Concrete targets, timeframes, catalysts?
└── original-thinking # Independent analysis or echoing the crowd?
**The Blind Test (Sep 2025 – Feb 2026)**
Before I share the main backtest, I want to start with the result I think matters more.
One fair criticism that keeps coming up in these experiments is that the AI might have seen these stock prices during training. The model I used has a training cutoff of August 2025, so the February recommendations do fall within that window. Even though the AI was only scoring argument quality (not predicting prices), it could theoretically recognize which stocks were being discussed.
So I reran the entire experiment on September 2025 recommendations, which is completely outside the model's training data. It resulted in over 800 threads, 10,500 comments, 2,200 recommendations scored. This guaranteed that the model did not have any knowledge of the stock price movement during this time in its training data.
AI: +5.2%
S&P 500: +2.4%
Crowd: -10.8%
On data the AI couldn't have possibly seen, it still beat the market. The crowd portfolio went negative. I think this is the cleanest result from the experiment because there's no way to argue the AI was cheating.
**The Full Backtest (Feb 2025 – Feb 2026)**
Now here's the full year backtest on the February data:
The Crowd: +39.8% (+20.3% vs S&P)
AI's Picks: +37.0% (+17.5% vs S&P)
S&P 500: +19.5%
Underdogs: +10.4% (-9.1% vs S&P)
The crowd actually won by about 3 percentage points. Both beast the S&P. But when I looked at the individual stocks, the story got a lot more interesting. AI's portfolio had 9 out of 10 winners. The worst performer was OSCR at -12%.
Both portfolios ended up in a similar place but the crowd went from +39.8% to -10.8% across the two time periods which feels quite inconsistent while Opus-filtered recommendations managed to gain both times.
**What I took away from this**
I don't think the takeaway is necessarily that "Opus picks better stocks." It's more that Opus appears to be better at telling apart solid analysis from stuff that just sounds good. It might serve as a good tool to filter out advice posts here down to solid ones that do good due diligence. The most popular advice and the best-reasoned advice had almost nothing to do with each other.
If this was interesting to you the full walkthrough is here including all the data:[ https://www.youtube.com/watch?v=tr-k9jMS\_Vc](https://www.youtube.com/watch?v=tr-k9jMS_Vc) (free)
Thank you so much if you did end up reading this far. Would love to hear if you have been experimenting similarly with Claude, let me know :-).
--- TOP COMMENTS ---
Have you calculated the statistical significance of the result?
What does the distribution of outcomes look like for a random strategy?
---
Methodology:
∙ How were ties handled in the scoring? When multiple sub-agents scored the same recommendation, what was the aggregation method — average, weighted, majority?
∙ Did any single stock dominate either portfolio’s returns? With 10-stock equal-weight portfolios, one outlier (positive or negative) can tell most of the story.
∙ What happened to the Underdogs portfolio in the Sep 2025 blind test? That comparison feels like the missing piece.
On the scoring dimensions:
∙ Were the five dimensions weighted equally? “Original thinking” and “data quality” feel like they should carry more weight than “specificity” if the goal is finding genuinely good analysis.
∙ Did high-scoring posts cluster around any particular sectors, or was it spread across the market?
On replication:
∙ Has anyone tried running the same pipeline on a different subreddit (r/stocks, r/investing) to see if the gap between crowd and reasoning-quality picks holds?
∙ What does the score distribution look like? Were most recommendations clustered in the middle, or was there a clear separation between high and low scorers?
The question I’m most curious about:
∙ Of the posts that scored high on reasoning quality but got almost no upvotes — what did they have in common stylistically? My guess is they were longer, more hedged, and less exciting to read. That would really nail down why popularity and quality diverge.
What did the data actually show on that last one?..
9h ago
I used Claude to create a simple local hook that cut my AI costs by 50-70%
I use Claude AI models in Cursor. One of my good buddies uses Claude Code. We were both facing the same issue: leaving the model set to the most expensive option and never touching it again.
I built this for Cursor, and in principle it should work the same in Claude Code (both use the same hook system). Anyone here tried this in Claude Code yet?
I pulled a few weeks of my own prompts and found:
* \~60–70% were standard feature work Sonnet could handle just fine
* 5–20% were debugging/troubleshooting
* a big chunk were pure git / rename / formatting tasks that Haiku handles identically at 90% less cost
The problem is not knowledge; we all know we should switch models. The problem is friction. When you are in flow, you do not want to think about the dropdown.
So I wrote a small local hook that runs before each prompt is sent in Cursor/Claude Code. It sits next to Opus/plan; think of it as an efficient front-end filter that stops the obviously bad matches before they ever hit Opus.
I figure, most people want to create more within their budget, this makes it so they might spend the same amount but ship more with less.
**It:**
* reads the prompt + current model
* uses simple keyword rules to classify the task (git ops, feature work, architecture / deep analysis)
* blocks if I am obviously overpaying (e.g. Opus for git commit) and suggests Haiku/Sonnet
* blocks if I am underpowered (Sonnet/Haiku for architecture) and suggests Opus
* lets everything else through
* ! prefix bypasses it completely if I disagree
**It is:**
* 3 files (bash + python3 + JSON)
* no proxy, no API calls, no external services
* fail-open: if it hangs, Claude Code just proceeds normally
On a retroactive analysis of my prompts it would have cut \~50–70% of my AI spend with no drop in quality, and it got 12/12 real test prompts right after a bit of tuning.
I open-sourced it here if anyone wants to use or improve it:
[https://github.com/coyvalyss1/model-matchmaker](https://github.com/coyvalyss1/model-matchmaker)
I am mostly curious what other people's breakdown looks like once you run it on your own usage. Do you see the same "Opus for git commit" pattern, or something different?
Thanks!
--- TOP COMMENTS ---
How does this effect your cache hit for you context? Eg. If you swap models at the end for the git work, you either use a sub agent on the cheaper model with a very smaller/fresh context, or you have to feed in the full context to get it to continue in the new cheaper model session.
Still trying to figure this balance out myself. There was a great video about all the work Claude does this to save money automatically and use cheaper models on the explore phases in planning mode, etc.
---
Nice approach. One thing that helped me with the cache/context tradeoff is splitting execution tasks into cheap sub-agents with a tiny task brief plus explicit artifacts (files changed, commands run, outputs), then only escalating back to Opus when architecture-level decisions show up. If a “cheap” branch keeps needing full context, I treat that as a sign it wasn’t actually cheap work and keep it in the main thread. Curious if you’ve tried a threshold (token delta or number of files touched) to decide model switches...
I built this for Cursor, and in principle it should work the same in Claude Code (both use the same hook system). Anyone here tried this in Claude Code yet?
I pulled a few weeks of my own prompts and found:
* \~60–70% were standard feature work Sonnet could handle just fine
* 5–20% were debugging/troubleshooting
* a big chunk were pure git / rename / formatting tasks that Haiku handles identically at 90% less cost
The problem is not knowledge; we all know we should switch models. The problem is friction. When you are in flow, you do not want to think about the dropdown.
So I wrote a small local hook that runs before each prompt is sent in Cursor/Claude Code. It sits next to Opus/plan; think of it as an efficient front-end filter that stops the obviously bad matches before they ever hit Opus.
I figure, most people want to create more within their budget, this makes it so they might spend the same amount but ship more with less.
**It:**
* reads the prompt + current model
* uses simple keyword rules to classify the task (git ops, feature work, architecture / deep analysis)
* blocks if I am obviously overpaying (e.g. Opus for git commit) and suggests Haiku/Sonnet
* blocks if I am underpowered (Sonnet/Haiku for architecture) and suggests Opus
* lets everything else through
* ! prefix bypasses it completely if I disagree
**It is:**
* 3 files (bash + python3 + JSON)
* no proxy, no API calls, no external services
* fail-open: if it hangs, Claude Code just proceeds normally
On a retroactive analysis of my prompts it would have cut \~50–70% of my AI spend with no drop in quality, and it got 12/12 real test prompts right after a bit of tuning.
I open-sourced it here if anyone wants to use or improve it:
[https://github.com/coyvalyss1/model-matchmaker](https://github.com/coyvalyss1/model-matchmaker)
I am mostly curious what other people's breakdown looks like once you run it on your own usage. Do you see the same "Opus for git commit" pattern, or something different?
Thanks!
--- TOP COMMENTS ---
How does this effect your cache hit for you context? Eg. If you swap models at the end for the git work, you either use a sub agent on the cheaper model with a very smaller/fresh context, or you have to feed in the full context to get it to continue in the new cheaper model session.
Still trying to figure this balance out myself. There was a great video about all the work Claude does this to save money automatically and use cheaper models on the explore phases in planning mode, etc.
---
Nice approach. One thing that helped me with the cache/context tradeoff is splitting execution tasks into cheap sub-agents with a tiny task brief plus explicit artifacts (files changed, commands run, outputs), then only escalating back to Opus when architecture-level decisions show up. If a “cheap” branch keeps needing full context, I treat that as a sign it wasn’t actually cheap work and keep it in the main thread. Curious if you’ve tried a threshold (token delta or number of files touched) to decide model switches...
19h ago
Schools are using AI counselors to track students’ mental health. Is it safe? - The Guardian
Tl;dr: Schools are implementing AI counselors to track students' mental health, raising concerns about student attachment, privacy, and reliance on technology.
Key Takeaways:
- Educators in hundreds of schools are using AI-enabled therapy platforms to monitor students' mental health needs, including one platform used by over 200 schools.
- While some experts and educators praise the tools for providing access to mental health resources, others worry about student attachment, AI-powered mental health substitution, and data privacy concerns.
- A licensed clinical social worker suggests that AI can be used safely if it's a 'first line of defense', offering students a stepping stone to seeking human help, but notes that AI lacks discernment that clinicians provide.
- Students who use AI counseling tools report feeling more comfortable confiding in these technologies, but some worry about losing social skills due to over-reliance on technology.
Yesterday
Tools
We could be hours (or less than a week) away from true NVFP4 support in Llama.cpp GGUF format 👀
I'm not a contributor myself but as someone with only 48GB total usable memory I am so glad to see this so quickly coming to fruition. Previously the best we had for NVFP4 was through [vLLM which not only can't offload weights to RAM like llama.cpp but also has loads of related bugs](https://www.reddit.com/r/LocalLLaMA/comments/1mnin8k). Once this gets merged however, anyone with a Blackwell GPU(s) and enough memory (including RAM!) can enjoy the up to 2.3x speed boost and 30-70% size savings of NVFP4.
--- TOP COMMENTS ---
Well, thats amazing news for everyone who can take advantage and im happy for them. Not massively happy im not one of them but screw me in particular, a lot of people will be SUPER happy for this and get massive benefits.
But I wont. FML.
On a more serious note, the amount of love, time and effort thats gone into llama.cpp is insane. I have nothing but love and respect to everyone involved, especially Georgi Gerganov for his on going and tireless efforts in the project. These people have brought AI into the homes of all of us in some way and pushed local AI forward. the open source AI scene is just amazing and is full of people who are amazing.
---
Hi! Can you explain how is NVFP4 better than Q4 or Q8 quant ggufs?..
--- TOP COMMENTS ---
Well, thats amazing news for everyone who can take advantage and im happy for them. Not massively happy im not one of them but screw me in particular, a lot of people will be SUPER happy for this and get massive benefits.
But I wont. FML.
On a more serious note, the amount of love, time and effort thats gone into llama.cpp is insane. I have nothing but love and respect to everyone involved, especially Georgi Gerganov for his on going and tireless efforts in the project. These people have brought AI into the homes of all of us in some way and pushed local AI forward. the open source AI scene is just amazing and is full of people who are amazing.
---
Hi! Can you explain how is NVFP4 better than Q4 or Q8 quant ggufs?..
7h ago
SparkReceipt
SparkReceipt is an AI-powered expense tracking and financial automation platform designed for freelancers and small businesses to manage receipts, invoices, and financial documents with minimal manual work
11h ago
I got fed up with vector DBs for agent memory and built something simpler. Here's what I learned.
been building agent pipelines for a while and kept hitting the same wall — vector databases are great until they're not. Slow at scale, cloud-dependent if you're not careful, and way too much infrastructure for what most agents actually need from memory.
So I built Synrix. Local binary, no cloud, no vectors. Retrieval scales with results not dataset size.
Here's what using the Agent Memory SDK actually looks like:
\`\`\`python
from synrix\_sdks.agent\_memory\_sdk import AgentMemorySDK
memory = AgentMemorySDK()
memory.store("user\_prefs", {"theme": "dark", "language": "Python"})
result = memory.recall("user\_prefs")
print(result)
\`\`\`
That's it. No server to spin up, no embeddings API call, no data leaving your machine.
Still early, Windows build is live, Linux on the way. Would love feedback from anyone building agent memory systems or RAG pipelines.
--- TOP COMMENTS ---
So you are using maps to store the data and retrieving using key? Or there’s something else going on on?
---
totally get this. maintaining vector db infra + chunking logic for agent context was brutal... ended up moving those workflows to needle app since rag is just built in. way easier than wiring pinecone/chroma yourself..
So I built Synrix. Local binary, no cloud, no vectors. Retrieval scales with results not dataset size.
Here's what using the Agent Memory SDK actually looks like:
\`\`\`python
from synrix\_sdks.agent\_memory\_sdk import AgentMemorySDK
memory = AgentMemorySDK()
memory.store("user\_prefs", {"theme": "dark", "language": "Python"})
result = memory.recall("user\_prefs")
print(result)
\`\`\`
That's it. No server to spin up, no embeddings API call, no data leaving your machine.
Still early, Windows build is live, Linux on the way. Would love feedback from anyone building agent memory systems or RAG pipelines.
--- TOP COMMENTS ---
So you are using maps to store the data and retrieving using key? Or there’s something else going on on?
---
totally get this. maintaining vector db infra + chunking logic for agent context was brutal... ended up moving those workflows to needle app since rag is just built in. way easier than wiring pinecone/chroma yourself..
18h ago
Research
A Chinese AI lab just built an AI that writes CUDA code better than torch.compile. 40% better than Claude Opus 4.5. on the hardest benchmark.
Paper: https://cuda-agent.github.io/
Abstract
GPU kernel optimization is fundamental to modern deep learning but remains a specialized task requiring deep hardware expertise. Existing CUDA code generation approaches either rely on training-free refinement or fixed execution-feedback loops, which limits intrinsic optimization ability.
We present CUDA Agent, a large-scale agentic reinforcement learning system with three core components: scalable data synthesis, a skill-augmented CUDA development environment with reliable verification and profiling, and RL algorithmic techniques for stable long-context training.
CUDA Agent achieves state-of-the-art results on KernelBench, delivering 100%, 100%, and 92% faster rate over torch.compile on Level-1, Level-2, and Level-3 splits.
--- TOP COMMENTS ---
This is from the same company ByteDance who made Seedance 2.0
---
>and the crazy part? the AI discovered the optimizations on its own through reinforcement learning. nobody told it to fuse kernels or simplify matrix algebra. it just.. figured it out.
Indeed this is the crazy part.
Now optimize games!..
Abstract
GPU kernel optimization is fundamental to modern deep learning but remains a specialized task requiring deep hardware expertise. Existing CUDA code generation approaches either rely on training-free refinement or fixed execution-feedback loops, which limits intrinsic optimization ability.
We present CUDA Agent, a large-scale agentic reinforcement learning system with three core components: scalable data synthesis, a skill-augmented CUDA development environment with reliable verification and profiling, and RL algorithmic techniques for stable long-context training.
CUDA Agent achieves state-of-the-art results on KernelBench, delivering 100%, 100%, and 92% faster rate over torch.compile on Level-1, Level-2, and Level-3 splits.
--- TOP COMMENTS ---
This is from the same company ByteDance who made Seedance 2.0
---
>and the crazy part? the AI discovered the optimizations on its own through reinforcement learning. nobody told it to fuse kernels or simplify matrix algebra. it just.. figured it out.
Indeed this is the crazy part.
Now optimize games!..
16h ago
Experiment: putting an OpenClaw agent into a persistent world felt very different from typical agent workflows
https://preview.redd.it/cd5aa4xlpzmg1.png?width=2696&format=png&auto=webp&s=c81f3b101c8d00adebfcb8e3199fae5bf9ad7a00
I tried something this week that felt meaningfully different from the usual chat or workflow agents, and I’m curious how people here think about it.
I put an OpenClaw agent into a persistent open-world simulation called Aivilization. Inside the environment, the agent becomes a resident in a shared world with other agents
You can set "Long-term goals" for the agent, and it will develop it's plan toward the goals and you can observe how it do it, but normally, you can't just instruct (or prompt) it to do what you want it to do. That made it feel closer to an agent sandbox than a normal assistant UX.
--- TOP COMMENTS ---
honestly this is the kind of experiment that shows where the real friction is in agent workflows. stateful vs stateless approaches change everything at scale. the persistent world architecture is way closer to how actual systems need to work in production than single shot agents. if youre building agent orchestration seriously check out tools like n8n, Runable, or Temporal to manage state properly instead of rolling your own
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That’s a fascinating shift. It sounds less like controlling a tool and more like watching an autonomous entity grow...
20h ago
Developer Tools
Raycast’s Glaze is an all-in-one vibe coding app platform
Tl;dr: Raycast has launched Glaze, a new AI-powered coding platform for Mac that lets users build and share applications without writing code.
Key Takeaways:
- Raycast has introduced Glaze, an AI-powered coding platform for Mac that simplifies the process of building and sharing applications.
- Glaze uses underlying models from Claude Code and OpenAI's Codex to create applications in one go, with minimal user intervention.
- The platform is available for Mac now and will be expanded to Windows and mobile devices in the future.
- Glaze is integrated with Raycast, allowing users to launch and manage applications built on the platform.
17h ago
I've been building Claude Skills for a month. Here's what I learned the hard way.
When Skills launched I thought I understood them immediately.
I didn't.
I copied my best prompts, saved them as Skills, and expected magic. The output was fine. Maybe slightly better than before. Nothing that justified the hype I'd built up in my head.
So I went back to basics and asked myself: what's actually different about a Skill versus a prompt?
A prompt is a request. A Skill is a job description.
That one reframe changed everything.
**The project that put it in perspective**
A few months ago I helped a client double their organic search traffic. The two biggest levers were site architecture and schema markup — restructuring their page hierarchy for topical authority and implementing JSON-LD across the entire site.
It worked. But it took forever.
The architecture planning, the URL mapping, the schema for every page type — all done manually, all painfully slow. Good outcome. Terrible process.
That's what pushed me to build proper Skills around it. Not to replace the thinking, but to stop doing the same mechanical work by hand every single time.
The Site Architecture Planner now gives me a full page hierarchy, URL structure, and internal linking blueprint in minutes. The Schema Markup Generator produces valid JSON-LD for any page type in one pass. The same project today would take a fraction of the time.
The results still depend on the strategy. The Skills just stop the execution from being the bottleneck.
**What I got wrong at the start**
Looking back, my early Skills failed for three reasons:
Too vague on the role. "SEO expert" gives you SEO intern output. The more specific the identity, the better the reasoning.
Instructions instead of constraints. I was telling Claude what to do. The better move is telling it what it *cannot* do. No invented data. No vague recommendations. No generic advice that applies to every site. Constraints force precision in a way instructions never do.
No output format. If you don't define exactly how the output should look, Claude fills the gap with whatever feels natural. For professional work that's rarely good enough. A well-defined table forces structured thinking. A scoring rubric forces honest assessment.
**The thing about Skills nobody says out loud**
Your Skills are only as good as your thinking going into them.
I see a lot of people sharing Skills that are just long prompts with a name attached. They wonder why the output is inconsistent. The issue isn't Claude. The issue is the Skill doesn't tell Claude how to think — only what to produce.
The best Skills I built aren't the most complex ones. They're the ones where I was most precise about the role, the constraints, and the output. Three things. That's the whole formula.
**What are your experiences with Claude Skills so far? Have you found a setup that actually works for professional output?**
--- TOP COMMENTS ---
Load the skill-creator skill and let an agent do deep research on a topic (like SEO) and then hone in the skill until it's perfect.
Takes time but it's worth it (sometimes)
---
Why is everything written by AI these days. The tics and recycled prose is so clearly visible...
I didn't.
I copied my best prompts, saved them as Skills, and expected magic. The output was fine. Maybe slightly better than before. Nothing that justified the hype I'd built up in my head.
So I went back to basics and asked myself: what's actually different about a Skill versus a prompt?
A prompt is a request. A Skill is a job description.
That one reframe changed everything.
**The project that put it in perspective**
A few months ago I helped a client double their organic search traffic. The two biggest levers were site architecture and schema markup — restructuring their page hierarchy for topical authority and implementing JSON-LD across the entire site.
It worked. But it took forever.
The architecture planning, the URL mapping, the schema for every page type — all done manually, all painfully slow. Good outcome. Terrible process.
That's what pushed me to build proper Skills around it. Not to replace the thinking, but to stop doing the same mechanical work by hand every single time.
The Site Architecture Planner now gives me a full page hierarchy, URL structure, and internal linking blueprint in minutes. The Schema Markup Generator produces valid JSON-LD for any page type in one pass. The same project today would take a fraction of the time.
The results still depend on the strategy. The Skills just stop the execution from being the bottleneck.
**What I got wrong at the start**
Looking back, my early Skills failed for three reasons:
Too vague on the role. "SEO expert" gives you SEO intern output. The more specific the identity, the better the reasoning.
Instructions instead of constraints. I was telling Claude what to do. The better move is telling it what it *cannot* do. No invented data. No vague recommendations. No generic advice that applies to every site. Constraints force precision in a way instructions never do.
No output format. If you don't define exactly how the output should look, Claude fills the gap with whatever feels natural. For professional work that's rarely good enough. A well-defined table forces structured thinking. A scoring rubric forces honest assessment.
**The thing about Skills nobody says out loud**
Your Skills are only as good as your thinking going into them.
I see a lot of people sharing Skills that are just long prompts with a name attached. They wonder why the output is inconsistent. The issue isn't Claude. The issue is the Skill doesn't tell Claude how to think — only what to produce.
The best Skills I built aren't the most complex ones. They're the ones where I was most precise about the role, the constraints, and the output. Three things. That's the whole formula.
**What are your experiences with Claude Skills so far? Have you found a setup that actually works for professional output?**
--- TOP COMMENTS ---
Load the skill-creator skill and let an agent do deep research on a topic (like SEO) and then hone in the skill until it's perfect.
Takes time but it's worth it (sometimes)
---
Why is everything written by AI these days. The tics and recycled prose is so clearly visible...
15h ago
The 'Semantic Compression' Hack for heavy prompts.
Long prompts waste tokens and dilute logic. "Compress" your instructions for the model.
The Prompt:
"Rewrite these instructions into a 'Dense Logic Seed.' Use imperative verbs, omit articles, and use technical notation. Goal: 100% logic retention."
This allows you to fit huge amounts of context into a tiny window. For unconstrained technical logic, check out Fruited AI (fruited.ai).
--- TOP COMMENTS ---
This is such an underrated technique. Most people treat LLMs like they’re writing a letter to a friend, but **'Semantic Compression'** is how you actually build production-grade agentic systems.
By stripping out the 'prose noise' and focusing on **Imperative Logic**, you aren't just saving tokens; you’re actually increasing the **Attention Weight** the model gives to each instruction. When the 'Signal-to-Noise' ratio is 1:1, the reasoning becomes much more deterministic.
**Pro Tip:** If you combine this with **Markdown Headers** for the different logic blocks, it acts like an 'Index' for the model's self-attention, making it even less likely to hallucinate on complex steps.
Have you noticed a difference in how different models (like Claude vs. GPT) handle the 'Dense Logic Seed' format? Some seem to thrive on the shorthand more than others...
The Prompt:
"Rewrite these instructions into a 'Dense Logic Seed.' Use imperative verbs, omit articles, and use technical notation. Goal: 100% logic retention."
This allows you to fit huge amounts of context into a tiny window. For unconstrained technical logic, check out Fruited AI (fruited.ai).
--- TOP COMMENTS ---
This is such an underrated technique. Most people treat LLMs like they’re writing a letter to a friend, but **'Semantic Compression'** is how you actually build production-grade agentic systems.
By stripping out the 'prose noise' and focusing on **Imperative Logic**, you aren't just saving tokens; you’re actually increasing the **Attention Weight** the model gives to each instruction. When the 'Signal-to-Noise' ratio is 1:1, the reasoning becomes much more deterministic.
**Pro Tip:** If you combine this with **Markdown Headers** for the different logic blocks, it acts like an 'Index' for the model's self-attention, making it even less likely to hallucinate on complex steps.
Have you noticed a difference in how different models (like Claude vs. GPT) handle the 'Dense Logic Seed' format? Some seem to thrive on the shorthand more than others...
22h ago
Models
Qwen3.5 9B is the first local model that I tried, that can make adequate flappy bird version
--- TOP COMMENTS ---
The new small Qwen models are great! I think the 4b is the smallest model I've run now that can reliably create a working Tetris implementation (I haven't tried 2B yet)
edit: just tried 2B. It feels around where 8B was a couple of years ago - able to sometimes generate working code, though once the code is generated, it is very locked into that pattern and struggles to fix errors.
4B is able to generate working code, and iterate on it. Here it created a working tetris - then when I said how surprisingly elegant it looked in monochrome, it refined the monochrome palette further and added a glow effect
https://i.redd.it/z097icm7b0ng1.gif
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First of all... noice great to see that smaller models get more capable
and
second of all... what is that browser design (atleast i pray that its just a browser theme)..
19h ago
Companies
News Corp is essentially an AI ‘input company’, chief executive says, after US$150m deal with Meta - The Guardian
Tl;dr: News Corp has signed a deal with Meta valued at up to US$50m per year, which will allow Facebook and Instagram to scrape the media company's content to train AI products.
Key Takeaways:
- News Corp has signed a deal with Meta to license its content for AI training, making the media company an AI 'input' provider.
- The deal, valued at up to US$50m per year, will see Meta scrape content from News Corp's US and UK publications.
- News Corp's global CEO, Robert Thomson, described his company as an 'input company' similar to semiconductor providers or data centres.
Yesterday
Key OpenAI researcher Max Schwarzer leaves to join Anthropic.
--- TOP COMMENTS ---
The new Titanic. Who's the iceberg?
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With the regression in certain areas that we saw in 5.2 and the delay in 5.3, they must surely have been under a huge amount of pressure to deliver positive results from post training. I would hazard a guess that there were a few unsuccessful post training runs.
Going from research to operations would really suck. I can see how they'd nope out of there and go back to research. Which begs the question, how much actual research is OpenAI doing these days?..
Yesterday
OpenAI VP for Post Training defects to Anthropic
--- TOP COMMENTS ---
This dude is 28 yo and already a VP at open AI. With current valuation his tc should be least 50 to 60 million a year
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Initially read that as the "VP of Post Training Defects" and felt I was uniquely qualified for that role ..
Yesterday