chisel VS lila

My first instinct was to ask "Does this play well with CIRCT?" And thankfully they answer that right away in the README.

I'm personally of the opinion that there is a LOT of room for improvement in the hardware design tooling space, but a combination of market consolidation, huge pressure to meet deadlines, and an existing functional pipeline of Verilog/VHDL talent is preventing changes.

That's not to say "Verilog/VHDL are bad", because clearly they've been good enough to support nearly all of the wonderful designs powering today's devices. But it is to say, "the startup scene for hardware will continue to look anemic compared to the SaaS scene until someone gives me all of the niceties I have for building SaaS tools in software."

A huge amount of ideas (and entire designs) start off as software sims, which enables kernel/compiler engineers to start building out support for new hardware before it's manufactured.

There is some interesting work going on at SiFive building hardware with Chisel[1], as well as some interesting work lead by a professor at William and Mary to improve simulations[2].

1: https://www.chisel-lang.org

2: https://github.com/sarchlab/akita

Inspired by Scala. Which can do a whole lot more, and worse. The currently biggest competitor to decades old hardware description languages is a Scala DSL.

Already mentioned Chisel: https://www.chisel-lang.org/

I'm also a hobbyist. There are a number of alternative HDLs out there, and as hobbyists we can deviate more from the mainstream of (System)Verilog and VHDL if we desire, though you'll still need to be able to read them. In the past I've done Verilog, but lately I've been using SpinalHDL and have been really enjoying it. Its close relative Chisel also makes appearances in the RISC-V space.

What are your thoughts on other HDLs like Chisel or BlueSpec when it comes to better type checking?

I started playing with FPGAs and HDLs a couple years ago with no hardware design background (I'm mostly a software architect/engineer) and in the end found that a "higher-level" HDL suited me better.

I chose Chisel (https://www.chisel-lang.org/) an HDL based on Scala (technically a Scala DSL) which can provide many facilities to hardware generation.

I'd highly advise looking into it although also knowing Verilog helps a lot.

But some go further and ask "what if when we add a soldering station on top of it?"

In recent years Chisel HDL, RISC V, and SiFive came out of their architecture group, to name a few.

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Lichess - Online Chess Server. Scala, TypeScript

> the player who committed more blunders lost 86% of the time

In some sense this is almost tautological. While finding an exact definition for a chess blunder isn't straightforward, here is one example from the Lichess UI:

https://github.com/lichess-org/lila/blob/b527746b179cdde6438...

Basically, if you make a move which decreases your winning probability more than 14% over the best move, that's a blunder. But winning probability is a nonlinear function of stockfish centipawns. A drop in 100 centipawns when you're up 15 points isn't a blunder. When the game was equal, it is.

Point is, by the time you know it's a blunder you already know something about the outcome of that move, that it swung the winning probability by more than 14%. So the analysis is kind of just measuring some function of winning probability and saying that it is highly correlated with winning probability.

If you want to improve make it your goal to play the best chess you can, not increase an arbitrary number. Watch YouTube series like John Bartholomew's "Climb the Rating Ladder" for some general insight into what you might be doing wrong. Read Irving Chernev's "Logical Chess: Move By Move" to see the thinking process of high level players. Do lots of puzzles (I like lichess.org for puzzles). And always analyze your games. When you analyze make it your goal to find at least two things you could have improved.

The easiest way to play against Stockfish is perhaps on https://lichess.org/, but it's not the only chess engine that evaluates positions with a neural network.

Lichess.com

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