Give.org says Watsi did not want to be evaluated by the BBB's standards for charity accountability[0]. I never donate to anyone without good numbers on Give.org and I recommend everyone check out Give.org when considering donating to any charity.
Yet there is no accountability or 'reporting' under the tab 'Total Revenue and Expenses' about how much went into 'administration costs'/fundraising and how much that is in relation to donations.
> This charitable organization either has not responded to written BBB requests for information or has declined to be evaluated in relation to BBB Standards for Charity Accountability.
Hard to read anything into this, tbh. BBB isn't exactly the most meaningful signal in the first place and it's hard to blame organizations for not playing along.
Given wikimedia's ranking on this website, I am not so certain it's worth engaging. Obviously the "did not share info therefore guilty" mantra is somewhat true for charities. But "did share info therefore not guilty" is even more silly.
I expect the people asking for money to do good, to show that they are doing the good they said they'd do. I am essentially giving them money with almost no liability, I want them to show me that they are not wasting/splurging it. Perhaps I'm too harsh, but given the prevalence of grifters/scammers these days, I find non-transparent charities trustworthy.
> I want them to show me that they are not wasting/splurging it.
Why don't you apply this same attitude to for-profit organizations, which are by definition wasting your money?
This is just idle curiousity—non-profits play a very odd role in our society, and I don't generally give to any of them in lieu of direct donations. Obviously that doesn't scale....
Because I can judge the return of for-profit companies. At least the ones selling a product, especially a physical one. The ones that don't sell a gauge-able product should, to my judgement, ascribe to the level of transparency that charities should provide.
For an organization begging so aggressively for donations, Wikimedia got $150M in donations during July 1, 2020—June 30, 2021. They spend 2M on hosting, 67M on salaries, with 9M of it going to management, and pocketed 50M. Moreover, it's not clear how the 50M they spent on Programs is
split between:
1) building the technological and operating platform that enables the Foundation to function sustainably as a top global internet organization
(2) strengthening, growing, and increasing diversity of the Wikimedia communities
(3) accelerating impact by investing in key geographic areas, mobile application development, and bottom-up innovation, all of which support Wikipedia and other wiki-based projects
I do not think they need my money, and I am suspicious most of it will not go towards keeping wikipedia alive.
How does that work? How can they buy into a non-profit?
It's an intriguing idea though, it would be cool to see more non-profits being supported. Non-profit doesn't have to mean being terrible and inefficient.
I should not trust their "vibe-coded productivity/token cost saving hacks" but I should trust yours?
Save 30% token costs when using Claude Code, Codex, OpenCode for free - with open source, local semantic search. Works for small and large codebases and monorepos! Enterprise-ready and fully compliant via Ollama and SQLite-vec.
Releases v0.0.42 Latest last month
Why should I trust that what you're peddling isn't snakeoil?
I literally say you should take benchmarks with a grain of salt :)
> Of course, it's always dependent on statistical noise + host system load, and running sufficiently large benchmarks is simply too expensive, so take em with a grain of salt.
And the savings listed are coming from a benchmark harness that implements different OSS bugs one time with and one without lumen - in those cases the % saved are reproducible (caveat: it was on older models, Opus 4.6 I believe).
Also I explain WHY it saves tokens - because the model doesn’t have to brute force different terms until it finds the match it needs, but uses semantic „distance“ so the embedding does it for the model.
I just went through a lot of benchmarking and the only thing that seemed better than rg was chunkhound, which sounds similar to this project. Actually a small Jina embedding model actually did better than voyage AI, but took a long time to index. Also chunkhound doesn’t work well with worktrees. In the end, I decided to stick with rg.
Xet's tech is very interesting and I greatly enjoyed their blog posts and other technical writeups[0]
But in the era of frontier models and hardware designed for fast model loading (eg: LLM on a chip) I can't imagine that this is worth so much. There's *a lot* of competition to improve Git LFS.
So while I agree with you that this plays a role I also have to believe that it's to understand model usage by competition and thus stifle it.
git lfs just dumps your bin on s3, and is abandonoware since ms bought github, since github historically hosts on aws s3 and improving it would immediately benefit amazon. gh, the maintainers of it, is the only forge that does not allow clean-up unreachable artifact from the server.
I have a large CNC router. I can throw a whole sheet of plywood on it and program it to cut it with a level of precision and repeatability that I am physically unable to do with my hands/eyes. In addition to the knowledge necessary to operate the machine, there's a lot of engineering knowledge necessary to design something to be manufactured by a CNC.
A lot of woodworkers scoff at the use of CNC routers; there's a lot that scoff at the use of any power tools at all!
I don't really understand though why folks have this *need* to assert that there is only One True Way to do something and to poo-poo on someone else's interests. It all seems like straw man arguments to me. Even the title of TFA BORN AGAINST, OR WHY HOBBY PROGRAMMING COMMUNITIES ARE AGGRESSIVELY AGAINST LLM USAGE seems like ragebait.
I think the issue isn't that it's this huge ordeal for a person to do, but that if billions of people have to do this then that's a fundamental design flaw that could have been avoided.
I have some that I got to connect to my MBP, which supports TB5. Unfortunately my MSI MPG 321URX doesn't properly support it, so half of the time the DisplayPort over USB negotiation doesn't work, and the monitor doesn't connect properly.
Apparently the majority of non-TB devices will have trouble with it. I can't even use the cable to charge my phone when attached to a wall wart!
> I can't even use the cable to charge my phone when attached to a wall wart!
It sounds like maybe you have a defective cable? When I first used the cable, it didn't charge my phone until I made sure it was plugged in all the way.
I have a weightlifting spreadsheet with weight on the vertical axis and reps on the horizontal axis. The value of each cell is the estimated 1 rep max if I accomplish that lift. In theory if my e1RM is 100kg then I can lift any permutation of (weight,reps) that have the same e1RM. This is akin to knowing Pareto Frontier of my current strength.
I use conditional formatting to color cells according to the probability that I can lift them—if I lifted 50kg for 10 reps then I can definitely do 50kg for 9 reps, so that cell is green. But if e1RM(50,10) > e1RM(40,15) then I can probably do that too so it's light green. The visualization naturally becomes Pareto-like.
If I'm feeling strong I can aim for higher weight, lower reps. Or if I'm feeling weak I can close out a (weight, reps) that's below my current e1RM but I haven't accomplished yet. The end result is that I'm always "accomplishing" some sort of PR no matter how I feel.
I wrote this app as a SPA! It uses a curve formulation similar to Brzycki, except I added a “shape” parameter (an exponent gamma between 0 and 1) that slopes the 1rm downwards at the right side.
My main finding for “pick whatever weight you want today” was that picking a lot of different weights made the curve less identifiable, so my latest iteration encourages you to pick a ladder for a few sentinel exercises per mesocycle in order to improve the statistical power. In addition, strength improves more quickly at >80% of 1RM, and hypertrophy depends on proximity to failure, so if you pick a lower weight, you really need to go to failure, which burns you out for the rest of your session, where leaving 1-2 reps in reserve is probably sufficient for hypertrophy and leaves a lot more gas in the tank for the rest of the session. Definitely open to suggestion/discussion here.
https://curvefit.app (it runs on Cloudflare free tier, so I won’t have to start running ads or charging until I hit a couple thousand users)
There’s no particular reason it’s not OSS, but my main interest is collecting a lot of data on different athletes and publishing original research. Most weightlifting studies are small n and over a short amount of time. My particular interest is how volume, load, and fatigue are related to strength, endurance, and compliance over time. My intention is to run it for a while, look at the data to generate some hypotheses, pre-register them, then run some experiments (and by that I mean just keep collecting data). If someone else was particularly interested in this goal, I would definitely invite them to the project. That’s why it was important for me to design it to be hosted for just the cost of the domain name, because I don’t really intend to make money from it, I’m just interested in the data.
I wouldn't call this a Pareto Frontier, but simple isolines through a 2d function.
There are weight x rep combinations that have a e1rm of 80kg, 85kg, 90kg, and so on.
These are just equal elevation contours through the e1rm(x, y) function.
The Pareto concept doesn't require that we calculate a function of all the dimensions and find contours; that sort of thing is not involved.
But we could apply it here like this. Suppose we conduct a weight lifting contest as follows: contestants can lift any weight any number of times, and record the weight and reps.
Then, how do we rank the results to find a winner, or winners? We have multiple dimensions, not a single dimension like "seconds to run 10 km".
We can find the Pareto front set of the performances by eliminating all that have been dominated. A lift is dominated if another lift is no worse (no less weight, and no fewer reps), and strictly better: eight the weight is higher, or there are more reps, or both.
We then end up with undominated winners, e.g. there could be three like this: { (100kg, 1), (80kg, 2), (70kg, 5) } but (70kg, 4) would not belong, due to being dominated by the third one, and (90kg, 1) would not due to being dominated by the first. The middle one is not dominated by either: though it's less weight than the 100kg, it is more reps, and though it is fewer reps than the 70kg, it is more weight.
Given the Pareto front set, if we want to determine a single winner, we need a function to reduce the parameters to a single value. (The function should be such that if we included the eliminated losers under that function, none of them would emerge winner over the Pareto front set). This e1rm function looks like it fits the bill.
If we have this function, we don't need the Pareto concept; we just run all the results through the function and pick the contestant(s) that maximize it.
The last time I tried this was back with Opus 4.6, and it was ok. I tried it with Fable 5 High just now and I was very impressed with the output. It took 7 minutes and one turn.
I'm not one to believe in all the one-shot hype, but this was pretty good.
Respectfully, it’s nothing new. Weightlifting industry has known this concept forever, it’s often just expressed as charts rather than graphs, as it is easier to interpret.
But they go even a step further, they extend into 3 dimensions to also add body weight as a variable. So your graph would really have to be a 3D volume. Because different levels of body weight have different capabilities.
Respectfully, his graph does not need 3 dimensions because it's a personal spreadsheet he uses just for his own training, so he can just display the data for his exact body weight.
Respectfully, that's a cool illustration of the idea of xRMs etc but is missing the whole point of programming for higher or lower reps. E.g. lower reps are more stressful / higher cost of recovery but more strength-specific; high reps are better for hypertrophy work. But then, any well designed program will have you working across a range of rep ranges and so on.
Some nuance here: the latest research shows that proximity to failure is the main hypertrophy driver regardless of load and rep count; high rep count makes proximity to failure harder to gauge; so high load/low reps close to failure is probably better for hypertrophy (there are other good reasons to do higher reps/lower load work though)
The most effective (difficult) training regimens usually avoid the middle of the distribution. You generally want to be operating at the extremes with some rotation schedule or duty cycle. High intensity interval training is an example of this philosophy that occurs within a single workout session.
If you want the most 'optimal' form of this (aka, hell on earth), you should purchase a rowing machine. Being able to engage with very aggressive, full-body exercise every single day without exceptions is almost like cheating biology. You can maintain a 2-3x VO2 max premium over your peers with very little risk of injury.
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