FL33TW00D

osy

TPM provides zero practical security

TPM (Trusted Platform Module) is as useful for preventing real attackers as the TSA is at preventing real terrorists. The architecture is fundamentally flawed and most existing implementations are completely broken. I thought this argument was settled decades ago[1] when "trusted computing" was introduced mostly as a way to provide DRM and ownership capabilities to organizations. It has largely failed to impact the consumer market when it was introduced back in the early 2000s. However, recently there seems to be a movement by certain parties to reintroduce this failed product back to the market. Microsoft argues that in order to use Windows 11, you need TPM 2.0 compatible hardware because[2]:

The Trusted Platform Module(TPM) requirement ena

cpursley

PostgreSQL is Enough

• Simplify: move code into database functions
• Just Use Postgres for Everything
• Just use Postgres
• PostgreSQL is the worlds’ best database
• Postgres is eating the database world
• Hacker News discussion

Background and Cron Jobs

rain-1

Purpose

Bootstrap knowledge of LLMs ASAP. With a bias/focus to GPT.

Avoid being a link dump. Try to provide only valuable well tuned information.

Prelude

Neural network links before starting with transformers.

vindarel

Developer experience, libraries, performance… (2021/11)

I'll preface this with three things. 1. I prefer schemes over Common Lisps, and I prefer Racket of the Schemes. 2. There is more to it than the points I raise here. 3. I assume you have no previous experience with Lisp, and don't have a preference for Schemes over Common Lisp. With all that out of the way... I would say Common Lisp/SBCL. Let me explain

1. SBCL Is by far the most common of the CL implementations in 2021. It will be the easiest to find help for, easiest to find videos about, and many major open source CL projects are written using SBCL
2. Download a binary directly from the website http://www.sbcl.org/platform-table.html (even for M1 macs) to get up and running (easy to get started)
3. Great video for setting up Emacs + Slime + Quick Lisp https://www.youtube.com/watch?v=VnWVu8VVDbI

Now as to why Common Lisp over Scheme

Ravenslofty

FlowMap: An Optimal Technology Mapping Algorithm for Delay Optimization in Lookup-Table Based FPGA Designs

• the classic paper
• compute LUT mappings through maximum-flow
• produces optimal depth designs
• but needs major area recovery, e.g. flow-pack
• implemented in the Yosys flowmap pass.

On Area/Depth Trade-off in LUT-based FPGA Technology Mapping

• relaxing requirement of one logic gate to one LUT allows area recovery
• duplicates logic to produce more LUT mapping opportunities

pervognsen

A quadratic space is a real vector space V with a quadratic form Q(x), e.g. V = R^n with Q as the squared length. The Clifford algebra Cl(V) of a quadratic space is the associative algebra that contains V and satisfies x^2 = Q(x) for all x in V. We're imposing by fiat that the square of a vector should be the quadratic form's value and seeing where it takes us. Treat x^2 = Q(x) as a symbolic rewriting rule that lets you replace x^2 or x x with Q(x) and vice versa whenever x is a vector. Beyond that Cl(V) satisfies the standard axioms of an algebra: it lets you multiply by scalars, it's associative and distributive, but not necessarily commutative.

Remarkably, this is all you need to derive everything about Clifford algebras.

Let me show you how easy it is to bootstrap the theory from nothing.

We know Cl(V) contains a copy of V. Since x^2 = Q(x) for all x, it must also contain a copy of some nonnegative reals.

davidscherer

import fdb, uuid, json, struct, sys

fdb.api_version(500)

class Collection:
    def __init__(self,
                 name,
                 indexes):
        self.name = name
        self.dir = root.create_or_open(db, name)

boneskull

These are example files from the tutorial by @boneskull as seen on Get on The Good Foot with MicroPython on the ESP32, Part 2

shagunsodhani

Efficient Estimation of Word Representations in Vector Space

Introduction

• Introduces techniques to learn word vectors from large text datasets.
• Can be used to find similar words (semantically, syntactically, etc).
• Link to the paper
• Link to open source implementation

Model Architecture