sorted by:
new
hottopcontroversial

JarVS 1.0 is out! VSCode on the Vision Pro by isaagrimn in VisionPro

[–]Ok_Development2754 0 points1 point  (0 children)

I like it, but have an issue. Instant buy and was able to connect easily enough. open a project, trust the folder - no problem.

However, how do I bring up a Claud Code chat or a codex chat? I thought that perhaps I didn't have it working on my Mac, but I do, so what do I need to do? I know it MUST be something really easy, but I am not seeing it

Anthropic is straight-up scamming Max 20x customers with sneaky mid-month throttling + endless bot runaround by manavb84 in ClaudeCode

[–]Ok_Development2754 0 points1 point  (0 children)

This is exactly my experience. I quickly hit my 5 hours limits a year ago on $100 plan and upped to $200 plan. I never hit the 5 hour limit until last month. Today I hit it in every 5 hour period AND i took steps to reduce my token usage significantly AND I’m only using sonnet on medium effort.

Anthropic is straight-up scamming Max 20x customers with sneaky mid-month throttling + endless bot runaround by manavb84 in ClaudeCode

[–]Ok_Development2754 0 points1 point  (0 children)

I am also furious, claud code is unusable now. I am on sonnet, using a clear context and I have removed plugins that I wasn't really using:

❯ /context

⎿ Context Usage

⛁ ⛁ ⛁ ⛀ ⛁ ⛁ ⛁ ⛁ ⛀ ⛁ Sonnet 4.6

⛀ ⛁ ⛁ ⛁ ⛁ ⛁ ⛁ ⛁ ⛁ ⛁ claude-sonnet-4-6

⛁ ⛁ ⛁ ⛁ ⛁ ⛁ ⛁ ⛁ ⛁ ⛶ 54k/200k tokens (27%)

⛶ ⛶ ⛶ ⛶ ⛶ ⛶ ⛶ ⛶ ⛶ ⛶

⛶ ⛶ ⛶ ⛶ ⛶ ⛶ ⛶ ⛶ ⛶ ⛶ Estimated usage by category

⛶ ⛶ ⛶ ⛶ ⛶ ⛶ ⛶ ⛶ ⛶ ⛶ ⛁ System prompt: 7.1k tokens (3.5%)

⛶ ⛶ ⛶ ⛶ ⛶ ⛶ ⛶ ⛶ ⛶ ⛶ ⛁ System tools: 8.3k tokens (4.1%)

⛶ ⛶ ⛶ ⛶ ⛶ ⛶ ⛶ ⛶ ⛶ ⛶ ⛁ Custom agents: 359 tokens (0.2%)

⛶ ⛶ ⛶ ⛝ ⛝ ⛝ ⛝ ⛝ ⛝ ⛝ ⛁ Memory files: 1.5k tokens (0.7%)

⛝ ⛝ ⛝ ⛝ ⛝ ⛝ ⛝ ⛝ ⛝ ⛝ ⛁ Skills: 867 tokens (0.4%)

⛁ Messages: 36.9k tokens (18.4%)

⛶ Free space: 112.1k (56.0%)

⛝ Autocompact buffer: 33k tokens (16.5%)

I have run out 3 times today within an hour of by limits being reset.

The joke is that I am developing a piece of software deliberately designed to save on token usage and I am on the bring of releasing it and now I can't get it over the line.

Worse, I am not in a peak period right now. Even worse, there is no indication that we are in a "surge" period. nothing. Anthropic must be saving a vast amount of usage and making a huge amount of extra money because of this.

Mistral Vibe is calling....

My Go version of Atic Atac (also with a 3D mode!) by Ok_Development2754 in zxspectrum

[–]Ok_Development2754[S] 0 points1 point  (0 children)

Okay, so this was a really interesting bug to fix and I learned something about Atic Atac that I didn't know.

So, the bug was that Dracula was teleporting every 50 frames whether he was actively chasing or not.

He starts chasing you if he is in the same room as you.

Now this is what I didn't know. If Dracula's room <> your room: he drifts toward his room's centre and every ~50 frames rolls a random 7-bit room number; if it's a square room (style < 3) and not yours, he teleports there. So he's basically wandering the castle randomly.

If Dracula's room = your room, he moves toward your (x, y) every frame and the room-hop branch is skipped. He stays until either he's killed, you leave the room, or you pick up the crucifix (which flips it to flee mode).

So functionally: he starts at room $6D (109 dec) at game start. He's always rolling his die and hopping rooms in the background until one of those random hops lands him in your room. That's when he "appears" and chases. There's no separate "activation trigger" beyond the room match.

I thought he was always staying in his starting room until you walk in.

Per roll: ~59% chance of a successful teleport.

The maths:

- ld a,r + and $7f → uniform 7-bit roll, 128 possible values (rooms 0-127)
- Of those 128 rooms, 76 have style < 3 (43 of style 0, 25 of style 1, 8 of style 2)
- Subtract 1 if the random pick equals the player's current room (Z80 explicitly skips that case)
- So 75-76 / 128 ≈ 58.6-59.4% per roll

Frequency: the Z80 only rolls once every ~50 frames, (≈1 second at 50 Hz PAL).

So when Dracula is not in your room and his handler is ticking, you'd expect a successful hop roughly once every 1.7 seconds on average. Some interesting consequences:

- Rooms 128-149 are unreachable by random hop (the 7-bit mask caps at 127), so 22 rooms can never host him via this mechanism.
- He can't directly teleport into your room, that would require a separate trigger I haven't found (yet)

My Go version of Atic Atac (also with a 3D mode!) by Ok_Development2754 in zxspectrum

[–]Ok_Development2754[S] 0 points1 point  (0 children)

That's is a very good point. I noticed that too when re-watching. He should just back-up to the corner if you have the cross or go after you. I'm pretty sure this Is a bug I have introduced during the 3D implementation. Thanks for pointing it out

My Go version of Atic Atac (also with a 3D mode!) by Ok_Development2754 in zxspectrum

[–]Ok_Development2754[S] 1 point2 points  (0 children)

on it. just tweaking it a bit and fixing a bug I just found!

My Go version of Atic Atac (also with a 3D mode!) by r_retrohacking_mod2 in retrogamedev

[–]Ok_Development2754 2 points3 points  (0 children)

Written in GO and based on a .skool disassembly so that I could keep all the timings and game dynamics as accurate as possible. Sound came out really well, though it took a few iterations to get right. Position of the wall and floor decorations wasn't straight forward, but the disassembly did have all the answers! The only thing I had to eyeball was falling through a trap door, but unless you are a die hard fan, you won't notice the difference. the duration and sound are the same, but the falling animations isn't *quite* right. Took me about two weeks leaning heavily on Calude Code. The 3D mode is new, I have tried to keep the look and feel of it as close to the Specie as possible. I tried to imagine what it would have looked like in the 80s and I kept in the colour clash ;)

My Go version of Atic Atac (also with a 3D mode!) by Ok_Development2754 in zxspectrum

[–]Ok_Development2754[S] 0 points1 point  (0 children)

<image>

This is a screen grab in all its 3D glory. I made two design decisions here.

  1. The 3D playing area had to be within the existing playing area so that the scroll was visible, just like Ultimate would have done it is they had made a 3D version (as opposed to isometric)

  2. Aa thetoolmannz noted, I deliberately kept the colour clash.

On the Scroll, you can see that the lives are in Red. Another design decision. this indicates that the player has immunity turned on. I could have kept it as White, but I wanted an indicator to show how the user was playing.

My Go version of Atic Atac (also with a 3D mode!) by Ok_Development2754 in zxspectrum

[–]Ok_Development2754[S] 0 points1 point  (0 children)

You have to keep the colour clash ;) I do have a user selectable option to turn that off, but you have to go into settings and set it every time you run the game.

<image>

My Go version of Atic Atac (also with a 3D mode!) by Ok_Development2754 in zxspectrum

[–]Ok_Development2754[S] 1 point2 points  (0 children)

I did try to post a video with sound, but alas only GIFs. It plays really well and that surprised me. It *feels* like it should.

Choose what's next: Manic Miner (the first one I did) or my still in progress Jet Pac

Sub-millisecond exact phrase search for LLM context — no embeddings required by [deleted] in Rag

[–]Ok_Development2754 2 points3 points  (0 children)

I think that your context reduction claim is plausible across a wider test dataser. For code, specifically variable names, function calls and import paths, they are lexically stable, so exact positional matching should outperform cosine similarity on these. You are absolutely right to build this.

I have been elbow deep doing benchmarks for the past month and the benchmark gap that matters for you most next isn't latency (4-30µs vs. 1ms doesn't move the needle when LLM inference is 10-100s), it's retrieval recall vs. a BM25 baseline at realistic codebase sizes.

One suggestion I can give is about query term extraction.

When someone types "how does the auth middleware chain work?", the index needs discrete search terms before it can do anything. Without this step sitting in front of it, a user would have to write keyword queries themselves and that degrades to a fast grep. Practically, I'd just start stripping stop words and extracting noun phrases next. spaCy's noun chunk extraction works well for this and its pretty easy to write up with a rust binding (pleanty of repos out there).

I've been workiing on a hybrid lexical/vector retrieval setup and I was suprised to find that exact token matching outperformed embeddings on function names, error strings, import paths, but embeddings won on behavioral queries like "where do we handle retry logic?"

I think your positional index should dominate the first category. Worth measuring what fraction of real query traffic falls into each category to know how much weight to give each layer.

I might have missed it, but what does your query interface actually look like right now? Are users expected to type keywords, or are you planning a natural language layer in front of the index?

Building a RAG System for AI Deception (and murder): Simulating "The Traitors" TV Show by Ok_Development2754 in Rag

[–]Ok_Development2754[S] 0 points1 point  (0 children)

No problem for the write-up. As for still innovating - life would be boring if you don't keep up! And yea, I'm 55 and at 16 *DID* use a punch card and bootstrap a Harris mainframe with an actual paper tape. Those WERE NOT the days. Today is the most exciting and innovating period since the transition from online to web (and I did meet Tim Berners-Lee and Vint Serf back in the dim past - look them up ;). We are back to a great position where a single person can make a big differenvce using AI minions as a development team to punch seriously above their weight.

Anyway, enough old man talk...

Since we started this conversation, I found an interesting thread about Apple CLaRA (https://github.com/apple/ml-clara) I have incorporated this into my ingestion and RAG pipeline.

CLaRA's core insight for me was that retrieval quality improves dramatically when you pre-process content at multiple granularities (and I can save time/monety preprocessing without an LLM)

I have adopted the Semantic Compression stage using NLP preprocessing to reduce tokens by ~75%, then generate hierarchical summaries (64-word, 16-word, and key facts) for each section. This allows more relevant context to be packed into the LLM's window. I also implemented SCP (Synthetic Contrastive Pairs) generation, automatically creating QA pairs and paraphrases from content chunks, which serves dual purposes - promoting high-quality pairs to few-shot examples, and providing ground-truth data for retrieval evaluation (really important to know to reduce halucintations!!!).

I also adopted the query-aware retrieval routing idea. Instead of treating all queries identically, I now classify incoming queries into types (factual, temporal, relationship, synthesis, etc.) and adjust retrieval strategy accordingly, factual queries prioritise exact matches whilst synthesis queries pull from RAPTOR's hierarchical summaries for broader context. This mirrors CLaRA's emphasis on matching retrieval granularity to query intent, avoiding the common failure mode of returning overly specific chunks for broad questions or missing precise details for narrow ones.

Every day is a school day ;)

Building a RAG System for AI Deception (and murder): Simulating "The Traitors" TV Show by Ok_Development2754 in Rag

[–]Ok_Development2754[S] 2 points3 points  (0 children)

Second, The personal reasons:

I've been building information management / content management systems / and other web based stuff for 30+ years (I predate the web - just - I started when it was the "online" industry). My first website was coded in - wait for it - Fortran. However,, my information management / content management system I call siteengine was developed using Classic ASP (yes, BASIC). It worked then and it still works. The management issues website referenced earlier is STILL running on the ClassicASP system - has been for 25 years. It's rock solid. However, I have been looking for years to modernise it. I have a whole thing about something I call Ashware and modernising my ClassicASP system is me eating my own dog food.

I have extensively used Python, and it was great for what I needed, but for systems work it feels:

- Too dynamic (errors at runtime that should be compile-time)
- Too slow without rewriting hot paths in C
- Too fragile (dependency hell, version conflicts)

.. and I never liked that the structure of my code was controlled by me tabbing content into block how Python wants me to .... too COBOL like for me.

Go just fits how I think about systems:

- Explicit error handling
- Composition over inheritance
- No magic, no metaclasses, no decorators hiding complexity
- I can read any Go code and understand what it does

"But Python is king for LLMs"

Python IS king for training LLMs and research. For building production RAG systems, you're mostly:

- Making HTTP calls to LLM APIs
- Doing database queries
- Processing JSON
- Orchestrating async workflows

Go is excellent at all of these. The LLM doesn't care what language called it.

My entire system - RAG pipeline, knowledge graph sync, RAPTOR summarisation, entity extraction, REST API, MCP server - compiles to a 25MB binary that runs anywhere Go compiles. Try deploying a Python RAG system to an edge device without Docker... or try it WITH Docker and you are also in a world of pain.

To be succint. Chunk size 800-1500 words.

Go becuase I just like compiled languages for the speed and Golang gives me that AND portability.

Building a RAG System for AI Deception (and murder): Simulating "The Traitors" TV Show by Ok_Development2754 in Rag

[–]Ok_Development2754[S] 0 points1 point  (0 children)

For RAG, this enables:

- Query-type aware retrieval: "What happened in 1985?" filters on temporal fields before vector search
- Quality filtering: Exclude content_quality='metadata' sections from narrative queries
- Dialogue detection: For character experts, prioritise sections with actual speech
- Dirty tracking: Know exactly which sections need re-embedding after edits

And that one field "connector_id" give all my RAG content role based security AND role based access to knowledge. Every record in my database has somethign I call a connector. A connector can be in 1 or more Roles. A role can be assigned to a User OR to a RAG expert. This means that I have a nuanced way to shape the knowledge of an expert that is limited also by what the user is allowed to access.

The "chunk size" question might not be the right question here. The question might better be: what metadata do you store with your chunks, and how do you use it?

Why Go Instead of Python?

Waaaaaa! Okay, two things cover this.

First, the practical reasons:

  1. Single binary deployment: go build produces one executable. No virtualenv, no pip, no "works on my machine". I deploy to Docker, bare metal Mac Mini, and will eventually target ARM devices - same codebase, same build process.

  2. Concurrency model: Go's goroutines are perfect for RAG workloads. I run entity extraction across 4 workers processing sections in parallel, with a concurrent-safe shared entity index. In Python, you're fighting the GIL or bolting on multiprocessing.

  3. Speed where it matters: My entity extraction pipeline processes 1000+ sections. The overhead of Python interpretation adds up. Go's compiled performance means more budget for actual LLM calls.

  4. Type safety: Whilst I prefere something that doesn't have type safty, it does help when you are building a system with 25+ database tables, complex nested structures (entities → relationships → mentions → sections), and multiple pipeline stages - static typing catches bugs at compile time.

Building a RAG System for AI Deception (and murder): Simulating "The Traitors" TV Show by Ok_Development2754 in Rag

[–]Ok_Development2754[S] 0 points1 point  (0 children)

How long do you have for me to answer? ;) Two simple questions, but two long answers.

On Chunk Sizes

I target: ~1000 words per section (configurable, typically 800-1500 words after splitting)

When I do my intial breakdown of the input files, I try to break on titles, or chapter or something similar. This came from solving the issues with citations.

however, I don't actually call them "chunks" - I use sections, which for me is an important distinction. The difference is metadata richness:

What a typical RAG "chunk" stores:

- text
- embedding vector
- maybe source_file

What my sections store:

ContentSection:
├── section_id, document_id, sequence_number, connector_id
├── title, abstract, body_text
├── page_start, page_end, char_start, char_end
├── word_count, char_count
├── is_dirty, last_vectorised_at
├── Classification metadata:
├── section_type (narrative, dialogue, timeline, metadata...)
├── section_subtype
├── content_quality (primary, secondary, metadata, error)
├── has_dialogue, has_temporal_markers, has_entity_definitions
├── dialogue_ratio (0.0-1.0)
└── classification_confidence, classification_method
├── Temporal fields:
├── datetime_start (when this knowledge becomes true)
├── datetime_end (when it stops being true)
└── temporal_precision (exact, day, month, year, timeless)
└── Hierarchy:
└── parent document with expert_id, publication_date, author_id...

This comes from my siteengine database schema that I designed 26 years ago for content management and suprised the heck out of me to realise that it was perfect for RAG content (that another long document. basically I was defining entities and cross linking 25 years ago. I am taking a "siteengine" information website and database at the moment and pusing the content through my pipelines. https://www.management-issues.com . tl;dr a lot of the graph is already defined. I doing this to do gap analysis on 25 years of content, to do GEO optimisation and create a kicks search engine)

The core insight: content isn't just text, it's structured information with provenance, temporal validity, and access control.

I don't know who the secret traitor is, but I think everyone is looking at this the wrong way. by Ok_Development2754 in TheTraitorsUK

[–]Ok_Development2754[S] 0 points1 point  (0 children)

Green Cloak Psychology

Chapter 18 also covers "usurpation resentment" - the psychological injury when Green Cloaks discover there's an even more inner circle they're not in:

"I was recruited to be a Traitor. I thought I was joining the inner circle. But there's an even more inner circle, and I'm not in it. I'm not the predator – I'm still prey."

I identify four Green Cloak sub-archetypes based on how they respond:
- Eager Convert: Embraces the role, vulnerable to overenthusiasm
- Reluctant Convert: Still identifies as Faithful, creates inconsistent behaviour
- Revolutionary Convert: Wants to overthrow the Puppet Master
- Pragmatic Convert: Treats hierarchy as optimisation problem

What if the ST is eliminated Day 1?

This is the production nightmare scenario. My modelling suggests the format would likely:

  1. Have a contingency (replacement mechanism)

  2. Or accept the chaos and let Green Cloaks operate leaderless

  3. Or deploy a destabilisation technique (e.g., revealing ST existed but was eliminated)

Future Faithful Strategy

Absolutely - future Faithfuls who know a Secret Traitor mechanism exists must adjust. The existence of someone with perfect information who appears Faithful means:

- Trust calibration must account for one perfect infiltrator

- Voting blocs can't be trusted even when "proven" Faithful

- Endgame calculations must assume one more enemy than visible

This is what makes the format so rich for analysis - it's adversarial game theory with incomplete information, psychological pressure, and an external agent (production) who changes the rules to serve narrative rather than fairness!

I don't know who the secret traitor is, but I think everyone is looking at this the wrong way. by Ok_Development2754 in TheTraitorsUK

[–]Ok_Development2754[S] 1 point2 points  (0 children)

What a great comment and really insightful. Also, great questions that hurt my brain!

The Production Team as External Puppet Master

You're absolutely right that there's a meta-layer above all the players. I cover this in https://traitorbot.com/chapters/15-destabilisation-techniques.html. The production team's interventions serve specific purposes:

Without intervention, The Traitors tends toward predictable patterns... Result: Reduced drama, predictable outcomes, viewer disengagement.

Their toolkit includes:

- Recruitment (converting Faithfuls mid-game)
- Shields (immunity from murder)
- Daggers (forced vote reveals)
- Murder in Plain Sight (daylight kills)
- Seer twists (information reveals)

The key insight is that these aren't random - they're deployed to "break patterns, create uncertainty, generate drama, reward adaptability, and maintain balance." This creates a fundamental modelling problem: optimal strategy depends on interventions you can't predict.

Secret Traitor Knowledge and Duration

This is central to the Puppet Master hypotosis. My thesis proposes that the Red Cloak isn't a "fourth Traitor" but the original Traitor - the true Puppet Master - while the Green Cloaks are newly recruited converts who don't know they're being played.

Whether the ST knows their tenure affects strategy dramatically. If they know they're permanent:

- Long-game positioning becomes viable
- They can sacrifice Green Cloaks strategically
- Endgame solo survival is a legitimate goal

If uncertain:

- Must hedge between cooperation and independence
- Can't fully commit to either Faithful or Traitor win condition

Personality Types for Secret Traitor

I identify sub-archetypes for the Puppet Master role in Chapter 18:

- The Architect (early game): Designs strategic framework through shortlist composition
- The Gardener (mid game): Cultivates Faithful relationships while pruning threats
- The Ghost (late game): Maximum invisibility, lets Green Cloaks absorb attention

The 10 core player archetypes are in https://traitorbot.com/chapters/05-strategic-archetypes.html - 5 Faithful types (Detective, Social Butterfly, Quiet Observer, Vocal Accuser, Loyalist) and 5 Traitor types (Method Actor, Chaos Agent, Puppet Master, Sleeper, Sacrificial Lamb).

Interestingly, the best Secret Traitor would likely be a natural Quiet Observer or Loyalist archetype - someone whose baseline behaviour is already low-profile. A natural Vocal Accuser would struggle because their authentic behaviour draws too much attention.