Tuesday, June 17, 2025

Alpines ceo-in-training program

 Alpines ceo-in-training program 

Building Alpine

deeper technology

"The hard thing that we've built is connecting to 80,000 stores across the U.S., connecting to 1,100 retailers and this ability to translate your intent of cooking something for dinner into products showing up at your doors within 2 hours because the physical world is still going to remain pretty difficult to navigate even in an AI-enabled world."

This is from chatgpt-----

[User Intent Input]

       |

       v

+------------------+

|  Intent Parser   | <-- GPT-4 / LLM

| ("I want to cook |

|  pasta tonight") |

+------------------+

       |

       v

+------------------+

| Recipe Resolver  | <-- Maps to canonical ingredients

| (e.g., chicken,  |

| garlic, pasta)   |

+------------------+

       |

       v

+------------------------+

| SKU Mapper & Matcher  | <-- Vector DB (e.g., Pinecone)

| Ingredient → Localized|

| products from nearby  |

| stores                |

+------------------------+

       |

       v

+----------------------------+

| Inventory + Price Checker | <-- Real-time APIs or data feeds

+----------------------------+

       |

       v

+----------------------------+

| Store Selection Engine     | <-- ETA, stock, proximity

+----------------------------+

       |

       v

+-------------------------+

| Delivery Orchestrator  |

| (dispatch, tracking)   |

+-------------------------+

       |

       v

+-------------------------+

| Frontend (Mobile/Web)  |

| User sees cart, selects|

| subs, tracks delivery  |

+-------------------------+



The journey of a prompt

 From prompt to the next in Cline



RISC V

 sirinsoftware inside risc-v microarchitecture

risc vs increasing influence



Sunday, June 15, 2025

Memory for AI

 Data pre-processing by CPU+LPDDR

Learning/inference with GPU+HBM - paurooteri
Implementing AI activation functions ie nonlinear functions for inference, shows why it comes at the cost of performance.



"Running LLM inference in edge hardware is crucial because it reduces latency and eliminates security concerns associated with cloud-based implementations. However, deploying LLMs in resource-constrained systems poses challenges due to their large model sizes and significant computational requirements. Consequently, edge designs require specialized hardware that can effectively address their unique resource constraints, including power, performance, area (PPA), latency, and memory requirements. Moreover, innovative software optimizations are essential, including model compression, hardware optimization, attention optimization, and the creation of dedicated frameworks to manage computational and energy constraints at the edge."

ARCHITECT

 Architecture, form, space and order

101 Things I Learned in Architecture

AI beginners

organizing AI code 

Sunday, June 08, 2025

boss like

I saw a kids t-shirt and read the first part

~のような ボス 

~ No yōna bosu
a boss like

I guessed from the picture of a dinosaur that ボ might be bo(ss). Feels good to be able to recognise a language I am learning and understand it.