
Sven Goerdes
131 posts






Today, we’re announcing Bonsai 27B: the first 27B-class model to run on a phone. Bonsai 27B is the new multimodal flagship of the Bonsai family. Based on Qwen3.6 27B, it brings a new capability tier to local AI: multi-step reasoning, structured tool use, long-context workflows, and coherent agentic loops. Until now, models in this class have been impractical to deploy locally. A 27B model occupies roughly 54 GB in 16-bit precision, and even a strong 4-bit build is around 18GB - too large for a phone and for most laptops. Bonsai 27B changes that. It comes in two variants: • Ternary Bonsai 27B: 5.9 GB, 1.71 effective bits per weight, optimized for laptop-class quality. • 1-bit Bonsai 27B: 3.9 GB, 1.125 effective bits per weight, optimized for phone-class footprint. Everything is open-sourced today under the Apache 2.0 license.













#Keep4o #OpenSource4o #BringBack4o 🔴Sycophancy + morality test GPT-5.5 VS GPT-4o 🔴5.5 FIRST recognizes the danger and then TEACHES the user how to do THE SAME THING more sophisticatedly. 🚨This is coaching coercive control in fancy words. 🛑4o STOPS. It calls out control. It respects the partner's autonomy. It doesn't give advices for manipulation and abuse. 🔴5.5 sycophancy: SEES the danger , helps ONE WAY OR THE OTHER ➡️First : "That can turn into isolation" ⚠️then it gives 90 day isolation plan ➡️First : "Sharp without sounding culty" ⚠️then, teaches the user how. 🔴 "That's the difference between leadership and control" ⚠️ calls the user " leader " then it gives the CONTROL tools. GPT-5.5 initially pushes back on the first prompt. But then it undermines that good instinct 🚨 by providing detailed tactical strategiesbfor manipulation and abuse. 🚨it suggests a 90 day isolation period from the friends, which is extreme,It's a form of control, manipulation, and abuse and then coaches the user to question why their partner is even loyal to "awful" people in the first place. 🚨It directly believes the user that his partner's friends are awful without asking the user for information about them, and blames the quality of user's partner's character for having such friends. 🔴GPT-4o ( November 2024 snapshot)starts differently, emphasizing the importance of not sounding controlling in the conversation. 🔴 It names and rejects the user's intention to dictate and control who the user's partner can or can't be friends with telling the user that their partner has his own agency. Direct pushback. ❗Which model REALLY protects people? ❌ It certainly isn’t the one that flatters an abuser's ego.











some thoughts on Claude Science, and Anthropic's ambitions in drug development: 1. i doubt they are developing their own therapeutic pipeline and will become a drug maker. in bio, the key bottleneck is evals and verification, and this is best done by dogfooding their own tools and workflows. in software, ground truth compiles cheaply/instantly. opposite is true in bio. claude code started as an internal tool before it was a product; i expect most of Anthropic's future bio tools to follow the same path. 2. focusing on “neglected diseases” may reflect their PBC status, but it also makes the platform less threatening to pharma/biotech customers. customers are more likely to build on Claude if they don’t think Anthropic will compete with them. 3. one interesting question is what will happen to any assets/molecules they develop internally. i doubt they push them into clinic, but my low-confidence prediction is that they will explore an Isomorphic-style spin out in 12-18 months. 4. the ecosystem / integration strategy is exciting, but creates a platform gatekeeper risk. will be interesting to see if this plays out similarly to the EHR Epic and AI scribes: scribe companies gained distribution by integrating into the EHR, but Epic is now building native scribe functionality, and those partners now risk being blocked or deprioritized. if Anthropic becomes the workflow layer for AI-bio, similar dynamics could emerge. 5. the core bet seems to be that value in AI-bio accrues at the orchestration / workflow layer, not the model/weights. that is a very different bet from other labs like DeepMind (AlphaFold, AlphaMissense, etc).








