Hey everyone, I'm a white belt and I admire Judo's premise of using minimum force to take down an opponent. That being said, when I go to randori, all of it goes out the window. I tense up and grip hard, and when I occasionaly throw it's without a proper lead-up/strategy.
How do go from Randori = seek and destroy, to Randori = practice and growth?
I saw the list that we have up for MAOI-friendly doctors and it only shows one doctor for the state of WA. Are there others that you know who can prescribe MOAIs?
Hey I'm in university, I use AI to quiz me on topics, to generate visual experiments (ex: to help me visualize topics) an for general explaining of topics. Does anyone have good ideas for how to use AI for school?
Writing my assignments: is a no thank you :) haha
Hey everyone,
Like a lot of you, I've missed assignments because they were buried three clicks deep in Canvas or hidden inside a syllabus PDF I forgot existed. So over the last few months, I built WizTribe.
It connects to Canvas and turns all your course information into a simple game plan:
All 1 click away for a change!
It also includes Dexter, an AI study assistant that can answer questions like:
Dexter only uses information already available in your course materials, announcements, and assignments.
Before launching, I'm looking for 10 students who use Canvas to help test it.
What’s the the deal?
What you get
If you're interested, send me a DM.
I'm looking for brutally honest feedback more than compliments.
Hey everyone,
Like a lot of you at LSC, I missed assignments because they were buried three clicks deep in Canvas or hidden inside a syllabus PDF I forgot existed.
So over the last few months, I built WizTribe.
It connects to Canvas and turns all your course information into a simple game plan:
All 1 click away for a change!
It also includes Dexter, an AI study assistant that can answer questions like:
Dexter only uses information already available in your course materials, announcements, and assignments.
Before launching, I'm looking for 10 students who use Canvas to help test it.
What’s the the deal?
What you get
If you're interested, send me a DM.
I'm looking for brutally honest feedback more than compliments.
Hey everyone,
Like a lot of you at UNT, I've missed assignments because they were buried three clicks deep in Canvas or hidden inside a syllabus PDF I forgot existed.
So over the last few months, I built WizTribe.
It connects to Canvas and turns all your course information into a simple game plan:
All 1 click away for a change!
It also includes Dexter, an AI study assistant that can answer questions like:
Dexter only uses information already available in your course materials, announcements, and assignments.
Before launching, I'm looking for 10 students who use Canvas to help test it.
What’s the the deal?
What you get
If you're interested, send me a DM.
I'm looking for brutally honest feedback more than compliments.
Hey everyone,
Like a lot of you, I've missed assignments because they were buried three clicks deep in Canvas or hidden inside a syllabus PDF I forgot existed.
So over the last few months, I built WizTribe.
It connects to Canvas and turns all your course information into a simple game plan:
All 1 click away for a change!
It also includes Dexter, an AI study assistant that can answer questions like:
Dexter only uses information already available in your course materials, announcements, and assignments.
Before launching, I'm looking for 10 students who use Canvas to help test it.
What’s the the deal?
What you get
If you're interested, send me a DM.
I'm looking for brutally honest feedback more than compliments.
Hey everyone,
Like a lot of you, I've missed assignments because they were buried three clicks deep in Canvas or hidden inside a syllabus PDF I forgot existed.
So over the last few months, I built WizTribe.
It connects to Canvas and turns all your course information into a simple game plan:
All 1 click away for a change!
It also includes Dexter, an AI study assistant that can answer questions like:
Dexter only uses information already available in your course materials, announcements, and assignments.
Before launching, I'm looking for 10 students who use Canvas to help test it.
What’s the the deal?
What you get
If you're interested, send me a DM.
I'm looking for brutally honest feedback more than compliments.
Hello UCF,
I realize you use Canvas. Like a lot of you, I've missed assignments because they were buried three clicks deep in Canvas or hidden inside a syllabus PDF I forgot existed.
So over the last few months, I built an app called WizTribe.
It connects to Canvas and turns all your course information into a simple game plan:
All 1 click away!
It also includes Dexter, an AI study assistant that can answer questions like:
Dexter only uses information already available in your course materials, announcements, and assignments.
Before launching, I'm looking for 10 students who use Canvas to help test it.
What’s the the deal?
What you get
If you're interested, send me a DM.
I'm looking for brutally honest feedback more than compliments.
has anyone switched from Parnate to EMSAM or Nardil or any other MAOI and felt better?
If so, tell us a bit about it
Hi, I'm a solopreneur and I'm building an app with a limited budget and cannot recruit people for a study. Are there automated and reasonably priced (or free) UX testing services that you tried and liked?
I'm building a mobile app. There are many UI templates out there that I can use, but I'm more concerned with User Experience (UX). What resources do you that I can use to ensure my mobile app is usable/intuitive for customers? Are there principles? tools that check UX? etc..
My friend is a Claude Code Junkie (as I am) and he has a Mac and uses a utility called RTK, and it saves a good deal of Claude tokens. I tried it on Windows and it didn't do much. Seems to have been badly retrofitted on Windows. Does anyone know a good token-saver utility (regardless of the "how") for those of us who spend way too much money on Claude Code on Windows?
//please don't give me a smartass comment about switching to MacOS. I tried before. I'm still looking for that app window I minimized by mistake years ago
I finished building a site that analyzes prompt accuracy, consistency and instruction following. It also helps you build production grade prompts. I validated with 10 or so users.
How can I find early users?
I am just learning that I shouldve found subscribers before building 🤦♂️
I finished building a site that analyzes prompt accuracy, consistency and instruction following. It also helps you build production grade prompts. I validated with 10 or so users.
How can I find early users?
So I was running some experiments and came across something wild. GPT-4o generated a token with 1.9% confidence when its own top pick had 97.6% confidence (see screenshot). Like it knew the answer and said the wrong thing anyway. It reminds me of the time when my ex-gf asked me if she should get a nose job. I knew the right answer should’ve been “no” but I said “yes” anyway. Probability wasn't on my side that day.
So this isn't a bug. It's by design. & let me explain:
When the LLM generates output, it doesn't always pick the highest likelihood next token as we’ve been told. At a model temperature > 0, the LLM samples from a probability, i.e. it rolls a rigged dice. In my example the 97.6% token (Wikipedia) wins most of the time. The 1.9% token (Information) wins rarely. I just witnessed a 1.9% dice roll win. But how does this actually work?
The hyperparameter that controls this, is temperature. Here's what it does to our example:
At Temperature = 0, the LLM always picks the top token. Deterministic. No vibes. Only math. All business. So in our case, it would’ve picked Wikipedia with no questions asked.
At Temperature = 0.9 (or anything 0 < x < 1), The LLM tightens the distribution. The 97.6% token jumps to ~98.6%, the 1.9% token drops to ~1.2%. The LLM becomes more of a pick-the-safe-answer cupcake.
AT Temperature = 1.0 → This is raw distribution, no changes. The 97.6/1.9 split you see is temp 1.0…. It stays that way, and normally this is the default.
At Temperature > 1. Ex: at 1.3 → This spreads things out. 97.6% drops to ~93%, 1.9% climbs to ~4-5%. All of a sudden the wrong answer is 2-3x more likely to get sampled. But this is where more creativity can happen. You’ll want to have a little more temperature if you’re wanting to generate a poem or a creative picture. But raise it high enough, and you’re in mushroom territory.
Temperature doesn't alter what the model believes is correct. It just changes how often the model acts on this belief vs. dives into the tail of the probability curve.
This is exactly why an all-business/deterministic LLM implementation sets temperature = 0 for anything requiring factuality and stability. It does not make the LLM smarter. But it stops the LLM from acting stoned and confidently saying the wrong stuff even though it knew better... i.e. hallucinating.
The model knew "Wikipedia." It said "Information." It rolled a dice and stuck with it.
I do the analysis on https://llmblitz.io
Finally, don't tell your girlfriend she needs a nose job. It's a trick question
—-----------------------In case you’re interested in the math —---------------------------
For all the nerds out there, here's the actual math. This article by Deepankar Singh explains how to perform the conversion
Step 1: start with logits. The model outputs raw scores ex in my case.:
"Wikipedia" → logit =3.71
"Information" → logit = -0.95
Step 2: divide by the temperature:
temp 1.0: 3.71 / 1.0 = 3.71, -0.95 / 1.0 = -0.95 ← My temperature
temp 0.9: 3.71 / 0.9 = 4.12, -0.95 / 0.9 = -1.06
temp 1.3: 3.71 / 1.3 = 2.85, -0.95 / 1.3 = -0.73
Step 3: softmax converts to probabilities/confidence: e^logit / Σe^logits
In my case:
Information: 1.9%
Wikipedia: 97.6%
So I was running some experiments and came across something wild. GPT-4o generated a token with 1.9% confidence when its own top pick had 97.6% confidence (see screenshot). Like it knew the answer and said the wrong thing anyway. It reminds me of the time when my ex-gf asked me if she should get a nose job. I knew the right answer should’ve been “no” but I said “yes” anyway. Probability wasn't on my side that day.
So this isn't a bug. It's by design. & let me explain:
When the LLM generates output, it doesn't always pick the highest likelihood next token as we’ve been told. At a model temperature > 0, the LLM samples from a probability, i.e. it rolls a rigged dice. In my example the 97.6% token (Wikipedia) wins most of the time. The 1.9% token (Information) wins rarely. I just witnessed a 1.9% dice roll win. But how does this actually work?
The hyperparameter that controls this, is temperature. Here's what it does to our example:
At Temperature = 0, the LLM always picks the top token. Deterministic. No vibes. Only math. All business. So in our case, it would’ve picked Wikipedia with no questions asked.
At Temperature = 0.9 (or anything 0 < x < 1), The LLM tightens the distribution. The 97.6% token jumps to ~98.6%, the 1.9% token drops to ~1.2%. The LLM becomes more of a pick-the-safe-answer cupcake.
AT Temperature = 1.0 → This is raw distribution, no changes. The 97.6/1.9 split you see is temp 1.0…. It stays that way, and normally this is the default.
At Temperature > 1. Ex: at 1.3 → This spreads things out. 97.6% drops to ~93%, 1.9% climbs to ~4-5%. All of a sudden the wrong answer is 2-3x more likely to get sampled. But this is where more creativity can happen. You’ll want to have a little more temperature if you’re wanting to generate a poem or a creative picture. But raise it high enough, and you’re in mushroom territory.
Temperature doesn't alter what the model believes is correct. It just changes how often the model acts on this belief vs. dives into the tail of the probability curve.
This is exactly why an all-business/deterministic LLM implementation sets temperature = 0 for anything requiring factuality and stability. It does not make the LLM smarter. But it stops the LLM from acting stoned and confidently saying the wrong stuff even though it knew better... i.e. hallucinating.
The model knew "Wikipedia." It said "Information." It rolled a dice and stuck with it.
I do the analysis on https://llmblitz.io
Finally, don't tell your girlfriend she needs a nose job. It's a trick question
—-----------------------In case you’re interested in the math —---------------------------
For all the nerds out there, here's the actual math. This article by Deepankar Singh explains how to perform the conversion
Step 1: start with logits. The model outputs raw scores ex in my case.:
"Wikipedia" → logit =3.71
"Information" → logit = -0.95
Step 2: divide by the temperature:
temp 1.0: 3.71 / 1.0 = 3.71, -0.95 / 1.0 = -0.95 ← My temperature
temp 0.9: 3.71 / 0.9 = 4.12, -0.95 / 0.9 = -1.06
temp 1.3: 3.71 / 1.3 = 2.85, -0.95 / 1.3 = -0.73
Step 3: softmax converts to probabilities/confidence: e^logit / Σe^logits
In my case:
Information: 1.9%
Wikipedia: 97.6%
So I was running some experiments and came across something wild. GPT-4o generated a token with 1.9% confidence when its own top pick had 97.6% confidence (see screenshot). Like it knew the answer and said the wrong thing anyway. It reminds me of the time when my ex-gf asked me if she should get a nose job. I knew the right answer should’ve been “no” but I said “yes” anyway. Probability wasn't on my side that day.
So this isn't a bug. It's by design. & let me explain:
When the LLM generates output, it doesn't always pick the highest likelihood next token as we’ve been told. At a model temperature > 0, the LLM samples from a probability, i.e. it rolls a rigged dice. In my example the 97.6% token (Wikipedia) wins most of the time. The 1.9% token (Information) wins rarely. I just witnessed a 1.9% dice roll win. But how does this actually work?
The hyperparameter that controls this, is temperature. Here's what it does to our example:
At Temperature = 0, the LLM always picks the top token. Deterministic. No vibes. Only math. All business. So in our case, it would’ve picked Wikipedia with no questions asked.
At Temperature = 0.9 (or anything 0 < x < 1), The LLM tightens the distribution. The 97.6% token jumps to ~98.6%, the 1.9% token drops to ~1.2%. The LLM becomes more of a pick-the-safe-answer cupcake.
AT Temperature = 1.0 → This is raw distribution, no changes. The 97.6/1.9 split you see is temp 1.0…. It stays that way, and normally this is the default.
At Temperature > 1. Ex: at 1.3 → This spreads things out. 97.6% drops to ~93%, 1.9% climbs to ~4-5%. All of a sudden the wrong answer is 2-3x more likely to get sampled. But this is where more creativity can happen. You’ll want to have a little more temperature if you’re wanting to generate a poem or a creative picture. But raise it high enough, and you’re in mushroom territory.
Temperature doesn't alter what the model believes is correct. It just changes how often the model acts on this belief vs. dives into the tail of the probability curve.
This is exactly why an all-business/deterministic LLM implementation sets temperature = 0 for anything requiring factuality and stability. It does not make the LLM smarter. But it stops the LLM from acting stoned and confidently saying the wrong stuff even though it knew better... i.e. hallucinating.
The model knew "Wikipedia." It said "Information." It rolled a dice and stuck with it.
I do my analysis on https://llmblitz.io --> check it out
Finally, don't tell your girlfriend she needs a nose job. It's a trick question
—-----------------------In case you’re interested in the math —---------------------------
For all the nerds out there, here's the actual math. This article by Deepankar Singh explains how to perform the conversion
Step 1: start with logits. The model outputs raw scores ex in my case.:
"Wikipedia" → logit =3.71
"Information" → logit = -0.95
Step 2: divide by the temperature:
temp 1.0: 3.71 / 1.0 = 3.71, -0.95 / 1.0 = -0.95 ← My temperature
temp 0.9: 3.71 / 0.9 = 4.12, -0.95 / 0.9 = -1.06
temp 1.3: 3.71 / 1.3 = 2.85, -0.95 / 1.3 = -0.73
Step 3: softmax converts to probabilities/confidence: e^logit / Σe^logits
In my case:
Information: 1.9%
Wikipedia: 97.6%
I have a website that analyzes hundreds of prompts everyday using logprobs and other signals. There are many reasons that make your prompt ignore you. Don’t take it personally, it’s not you, it's me probability. I run analysis on aggregate prompts with an agent (no I don’t read your prompts) and based on the analysis, here are the top 5 reasons LLMs SEEM to like their own ideas more than they like your instructions:
1. Negations are cooked, don't be negative
A negation instruction like “never add disclaimers" is not a rule, it's a suggestion that the model will fight against. RLHF training hammered "be safe and helpful" into every weight in every tensor. You're asking it to unlearn that with one sentence. You’re losing the probability game. Instead, flip it: "End every response with the answer only." Affirmations win, negotiations sit there and hope to be noticed.
2. LLMs respond to assertiveness, show them who's boss
"Try to be concise" → the model tries. Tries real hard. And then writes four paragraphs anyway because "try" left the escape hatch open. Every "ideally," "when possible," and "generally" in your prompt is a green light to ignore that instruction under pressure. Kill them all. No survivors. Be assertive.
3. Two rules are secretly fighting and the model is picking sides
"Preserve the original tone" + "rewrite in formal academic style" seems fine to you. At the token level, the model hits a word like "gonna" and genuinely doesn't know what to do, on my website there is a tool that shows how logprobs are split across both options, confidence craters, and it just... picks one. Usually wrong. Add an explicit tiebreaker or one of them has to go. You can’t have your cake and eat it.
4. RLHF domain pull is a thing and barely anybody talks about it
Tell the model it's a "Shakespearean translator" and it will default to the most ceremonial, ornate version of that style it has ever seen — because that's what dominated its training data for that domain. It's not following your prompt anymore, it's following its priors. Counter it explicitly: "When uncertain, choose direct force over ornament."
5. Buried instructions are pretty much invisible
"You should maintain a professional tone, avoid jargon, and always end with a summary" parsed as one vibe, not three rules. Prose paragraphs are read at lower attention weight than explicit list items. We literally see this in the token confidence data. If it matters, number it. If it's in a paragraph, it's decorative.
tl;dr your prompt isn't a contract, it's a suggestion box. structure it like you mean it or the model will freelance.
Also if you want, this is a tool on the site that can tell you why a certain instruction was ignored/overridden (there are many reasons). There is also this one that will analyze your prompt for both accuracy and consistency.
May the probabilities be with you.
I have a website that analyzes hundreds of prompts everyday using logprobs and other signals. There are many reasons that make your prompt ignore you. Don’t take it personally, it’s not you, it's me probability. I run analysis on aggregate prompts with an agent (no I don’t read your prompts) and based on the analysis, here are the top 5 reasons LLMs SEEM to like their own ideas more than they like your instructions:
1. Negations are cooked, don't be negative
A negation instruction like “never add disclaimers" is not a rule, it's a suggestion that the model will fight against. RLHF training hammered "be safe and helpful" into every weight in every tensor. You're asking it to unlearn that with one sentence. You’re losing the probability game. Instead, flip it: "End every response with the answer only." Affirmations win, negotiations sit there and hope to be noticed.
2. LLMs respond to assertiveness, show them who's boss
"Try to be concise" → the model tries. Tries real hard. And then writes four paragraphs anyway because "try" left the escape hatch open. Every "ideally," "when possible," and "generally" in your prompt is a green light to ignore that instruction under pressure. Kill them all. No survivors. Be assertive.
3. Two rules are secretly fighting and the model is picking sides
"Preserve the original tone" + "rewrite in formal academic style" seems fine to you. At the token level, the model hits a word like "gonna" and genuinely doesn't know what to do, on my website there is a tool that shows how logprobs are split across both options, confidence craters, and it just... picks one. Usually wrong. Add an explicit tiebreaker or one of them has to go. You can’t have your cake and eat it.
4. RLHF domain pull is a thing and barely anybody talks about it
Tell the model it's a "Shakespearean translator" and it will default to the most ceremonial, ornate version of that style it has ever seen — because that's what dominated its training data for that domain. It's not following your prompt anymore, it's following its priors. Counter it explicitly: "When uncertain, choose direct force over ornament."
5. Buried instructions are pretty much invisible
"You should maintain a professional tone, avoid jargon, and always end with a summary" parsed as one vibe, not three rules. Prose paragraphs are read at lower attention weight than explicit list items. We literally see this in the token confidence data. If it matters, number it. If it's in a paragraph, it's decorative.
tl;dr your prompt isn't a contract, it's a suggestion box. structure it like you mean it or the model will freelance.
Also if you want, this is a tool on the site that can tell you why a certain instruction was ignored/overridden (there are many reasons). There is also this one that will analyze your prompt for both accuracy and consistency.
May the probabilities be with you.
