[AINews] AI Discords Newsletter 12/1/2023

This is AI News! an MVP of a service that goes thru all AI discords/Twitters/reddits and summarizes what people are talking about, so that you can keep up without the fatigue. Signing up here opts you in to the real thing when we launch it 🔜

OpenAI Discord Summary

• Extensive dialogues across several channels focused on using and optimizing OpenAI's AI models, specifically, ChatGPT and GPT-4, for different applications. Users often compared performance, shared experiences ("Despite positive feedback, he wondered if a better AI model exists for Python and Flask programming"), and offered suggestions for better prompt engineering ("These guidelines emphasise clear, non-repetitive and well-defined instruction giving, understanding AI capabilities, and open communication with the AI").

• Notable discussion on AI integration in daily life, with vision of AI as a personal assistant or learning companion, proactive AI interaction, potential challenges and ethical considerations. Similarly, AI use cases for enhancing personal growth, productivity, and learning strategies using OpenAI and other AI resources were actively discussed.

• Several concerns and technical issues raised by users, such as image analysis through GPT, saving errors, reading encrypted information, usage cap, waiting list frustrations, login and account issues. A notable comment includes "Users @rubi747, @abgslayer, and @superstar3244 reported issues with usage limits on the OpenAI API despite apparently having balances in their accounts."

• AI's proficiency in tasks beyond text generation, notably examining and understanding images, was discussed. A quotation that stands out is "@donald_23__11175 expressed interest to know if OpenAI's [ChatGPT-4] could analyze images, specifically spotting weak points in dating profile images such as hair styles or expressions."

• Lastly, there was community interaction with survey invitations, guidelines for prompt engineering, question formulation and usage policies, as well as an ongoing debate about the AI's ethics and usage restrictions, contributing towards a vibrant discussion atmosphere across channels.

OpenAI Channel Summaries

▷ #ai-discussions (104 messages):

    - **ChatGPT as a Python Software Assistant**: `@taholmes160` shared his experience of using **ChatGPT 3.5 and 4.0** for Python software development. Despite positive feedback, he wondered if a better AI model exists for Python and Flask programming, sparking discussion among users like `@HeatOn`, `@eskcanta` and `@webhead`.

• AI Integration in Daily Life: Users @fettywhap and @exh0rt pondered over the concept of AI as an integral part of daily life. They envisioned it as a personal assistant or learning companion, interacting proactively with the user. They also discussed potential challenges and ethical considerations, such as fostering dependence or isolation.

• Exploring AI Use Cases and Learning Process: @fettywhap and @exh0rt engaged in an in-depth discussion on how to leverage AI (like OpenAI) for personal growth, learning, and productivity. Key strategies highlighted include analyzing learning habits, implementing efficient learning techniques, and prompt engineering.

• AI in Biomedical Sciences Study Survey: @alvaromartinezmateu invited the community to participate in a survey exploring public perception of AI applications in biomedical sciences.

• OpenML and Other Resources: @severus_27 shared a link (OpenML guide) as a resource for AI enthusiasts to learn from various open-source resources and free materials such as books, courses, papers, guides and more. Users also discussed potential issues with other AI technologies like Slab and Hugging Face.

• Artificial Image Generation: User @kelvin_zero commented on the limitations of AI image generation, stating that it couldn't generate a snake swallowing a fireball. His comment was accompanied by a link to an image.

▷ #openai-chatter (242 messages🔥):

    - **Performance Comparison between GPT Versions**: User `@mathy_h` mentioned experiencing worsening performance with **GPT-4** over time, pointing towards slower response times and fewer correct answers. User `@foxabilo` contradicted this, stating that GPT-4 is more powerful and has faster response times (from 4-5 tokens per second at launch to 40-50 tokens per second currently).

• Issues with GPT's Response Quality: Several users pointed out recent quality issues with GPT-4. @enigmaemmy mentioned that GPT-4 has become "dumber". @cook_sleep complained about GPT-4's inability to understand a simple question and design a computing program for it. There was a discussion on how different structures and clarity of prompts can affect the model's output.

• Waitlist for GPT-4: Users @megawhat2015 and @vincentdecod3rs expressed frustration over the waitlist for GPT4. @captainsonic and @satanhashtag responded that there is currently no estimated time of arrival for members to gain access and they should be patient.

• Utilizing ChatGPT4 for Professional Tasks: User @cirus2390 reported an error encountered at their organization after a security update had allowed them to use ChatGPT4. User @satanhashtag suggested solutions like disabling the proxy or trying different browsers.

• First Anniversary of ChatGPT: Numerous users wished ChatGPT a happy first birthday. They reflected on the AI's improvements and shared their anticipation for future developments.

▷ #openai-questions (158 messages🔥):

    - **Subscription Cancellations and Waitlist**: User `@feedes` asked about the effect of cancelling and then resubscribing to their ChatGPT Plus subscription. As no firm answer was given, the common consensus was to avoid cancellation due to a long waitlist for new subscriptions.

• Building a Chatbot with Link Generation Capability: User @elevansk asked for advice on building a chatbot that, in addition to generating answers, could also provide relevant Internet links for enhancing user learning experience.

• Usage Quotas and Errors: Users @rubi747, @abgslayer, and @superstar3244 reported issues with usage limits on the OpenAI API despite apparently having balance in their accounts. They were advised to get in touch with support for further assistance.

• Troubleshooting Extension-Related Issues: Users @solbus and @signate engaged in a lengthy troubleshooting session to address apparent issues with the ChatGPT web interface, which was not functioning as expected on certain browsers and possibly due to VPN or network filter issues.

• Custom GPT and API Actions: User @8020 reported difficulties making API "actions" work with custom GPTs, noting inconsistent behavior and empty responses from the API. User @bombenstein asked for clarifications on the Python API's error-handling mechanisms. In both cases, users were advised to use the developer channels or to wait for further support responses.

• Account and Login Issues: User @nic0m0d raised concerns regarding difficulties in changing authentication methods for their OpenAI account, raising a broader issue about the inflexibility of account settings and practices.

Note: No relevant URLs or blog posts were shared in the dialogues provided.

▷ #gpt-4-discussions (37 messages):

    - **GPT Analyzing Images**: User `@jungle_jo` expressed concern about his custom GPT's inability to analyze provided example images before producing new images. They shared that even with given instructions, the model fails to examine the provided images.

• GPT Saving Errors: User @tman42 brought up the issue of encountering consistent saving errors while updating their GPT, seeking if others are experiencing the same problem.
• GPT Use for Encrypted Information: A detailed discussion was provoked by @toby66_22688's inquiry about using GPT for reading and extracting information from passports and visas. While acknowledging the importance of privacy protection, @toby66_22688 emphasized that as a travel agency they have necessary customer permission to process such information. However, @martinr_33972 and @drcapyahhbara offered opposing views because of security concerns and potential data breach risks on platforms like OpenAI's ChatGPT.
• Usage Cap Queries: User @_vincent32 questioned why the usage of his self-built GPT playground was also restricted despite it supposedly having no usage cap, after his GPT-4 reached the usage limit.
• Potential of Custom GPT for Non-Paying Users: In a discussion between @whiffleball. and @elektronisade, it was clarified that the benefits of a custom GPT created using the API can indeed be extended to non-paid users. However, it was noted that the API itself is a paid service, but the possibility of charging for access was explored.
• Image Analysis Capabilities: @donald_23__11175 expressed interest to know if OpenAI's ChatGPT-4 could analyze images, specifically spotting weak points in dating profile images such as hair styles or expressions. User @satanhashtag affirmed that the analysis can be done using ChatGPT-4.
• Unavailability of Plugin Store: User @ardi83 reported that they couldn't find the plugin store in their GPT interface and sought help regarding the same.
• Human Verification Requests: User @kibbyd. complained about being frequently asked to confirm being human despite being a paid user. @elektronisade and @satanhashtag suggested it might be due to certain factors on the user's system that give an appearance of a bot, and recommended testing with another browser.

▷ #prompt-engineering (36 messages):

    - **Prompt Engineering Guidelines**: User `@exh0rt` synthesized a detailed list of guidelines for better prompt engineering from the previous discussions of GPT chats and input from other users. These guidelines emphasise clear, non-repetitive and well-defined instruction giving, understanding AI capabilities, and open communication with the AI. `@eskcanta` further added inputs and suggestions, emphasizing the need for instructing the AI to explain its understanding to aid in conflict resolution.

• Conversation Roleplay: User @eskcanta shared a novel approach to instructing the AI by making it assume a specific role (e.g., a friend named John) to inspect and provide feedback on the user's prompts in an interactive manner. @exh0rt felt this was an intriguing path to explore.

• Sharing Prompts: In ongoing interaction, @exh0rt expressed intention to share the v4 of a new prompt he's working on for feedback but also suggested not to clog the chat. In response to this, @eskcanta encouraged making a separate thread for it.

• Death Battle Queries: User @gentleman_crow expressed difficulty getting AI to answer hypothetical death battle situations between characters. @solbus reminded that OpenAI's usage policies restricts violent content.

• Concerns and Inquiry About OpenAI Fine-Tuning: @adx2061 expressed frustration with creating gpt instructions, while @rez0 posed a question about the effectiveness of OpenAI's fine tuning and how many examples were needed to gain effectiveness.

▷ #api-discussions (36 messages):

    - **GPT Instruction Guidelines**: In a discussion sparked by `@exh0rt`, the contributors (`@eskcanta` and `@exh0rt`) discussed strategies for providing instructions to OpenAI's GPT models. Key takeaways included avoiding conflicting and repetitive instructions, asking the AI to illuminate any instruction conflicts, allowing the AI 'to talk back' for conflict resolution, and being clear about expected behavior and outcomes. There was also an emphasis on using clear, specific language and avoiding jargon.

• Interactive Dialogue with GPT: @exh0rt sought ways to make the AI 'talk back' for better interaction. The discussion, led with @eskcanta, touched on strategies such as telling the AI to speak like a certain character and encouraging the AI to ask clarifying questions.

• Question Formatting: @exh0rt expressed uncertainty about how to determine and implement the best format for questions when working with GPT. @eskcanta advised first having a clear understanding of what the user wants the model to do before exploring different question formats.

• Violence in AI Response: User @gentleman_crow presented an issue with GPT-3.5's responses regarding violent content, to which @solbus reminded of OpenAI's usage policies prohibiting violent content across all services.

• Fine-Tuning OpenAI GPT: A user @rez0 queried about the efficiency of OpenAI's fine-tuning, questioning if a few examples were sufficient or if hundreds were necessary for effectiveness. The question remained unanswered in the provided content.

Nous Research AI Discord Summary

• Discussions about Machine Learning Models with Knowledge Graphs, with @euclaise sharing a paper on the subject and @max_paperclips suggesting the use of Graph Neural Networks (GNNs) to simplify graph lookups when combined with Reformulated Adjusted Google PageRank (RAG) and an external datastore.
• In-depth conversations on AI/ML career paths, motivations, and challenges with users like @teknium and @coffeebean6887 sharing their stories and @jaisel expressing concerns about career saturation. Videos on related topics also shared by @pradeep1148 and @jaisel.
• Coverage on the performance of multiple AI models like hf-causal-experimental, Deepseek 67b, Qwen 72b on systems of various configurations. Models and tasks expertise breakdown provided by @tokenbender and thoughts on bot post-processing of performances by @gabriel_syme.
• A range of interesting links shared, spanning topics from StableLM Model Size to NVIDIA's Superchip for Recommenders and GNNs. Discussion was also had on topics like Vector Quantization, Elo weirdness's impact on LLM rankings, and speculations around GPT-4.
• Benchmarks and Model Evaluations emerging as a hot topic, particularly between Hermes 2.5, Hermes 2, Qwen 72b, and Deepseek 67b. @teknium shared initial comparative benchmark data between these models. Introduction of a new dataset called HelpSteer, potential use of multi-language models and speculation over OpenAI's Compute Capability being further points of discussion.
• Queries and discussions about Training Decoder Only Models, PC Hardware for machine learning, enabling GPU in LM Studio, and experiences with OpenHermes 2.5 Finetuning in the context of llms. A full finetune example config shared by @erikrichardlarson.
• Clarification about project names and ownership in the collective cognition channel, with @yobibyte correcting the mention of a project to Open Access AI Collective also responsible for Jackalope, and @teknium confirming the owner as user <@257999024458563585>.

Nous Research AI Channel Summaries

▷ #ctx-length-research (5 messages):

    - **Machine Learning Models with Knowledge Graphs**: User `@euclaise` shared a relevant [paper](https://arxiv.org/pdf/2210.15497.pdf) on domain-specific knowledge graphs to enhance machine learning models.

• Relevance of Graph Neural Networks (GNNs): @max_paperclips suggested the possibility of combining Reformulated Adjusted Google PageRank (RAG) with an external datastore retrieved as a graph, where Graph Neural Networks (GNNs) could simplify graph lookups. However, it is noted that this approach would require more investigation.
• Executives Function Component over Language Model: @spaceman777 emphasized the potential of adding an executive function component or another method of dynamic weights/memory registers over language model-like machines (LLMs). He suggested this could be an answer to the limitations LLMs share with the Oracle of computation theory.
• Exploitation of Neo4j's Features and Capabilities: @spaceman777 expressed his anticipation to see the development of graph-based knowledge versus basic human-designed vector store retrieval hierarchies in the context of Neo4j. He also showed great hopes for the advancements in architectural modifications of existing models.
• Supervisory Systems for Improved Model Performance: @maxwellandrews agreed with the concept of having a supervisory system that can be cross-attended to multiple context regions. This, according to him, might add to the range and efficiency of machine learning models.

▷ #off-topic (37 messages):

    - **Career paths and entry into AI/ML**: Users shared their journeys into AI/machine learning, citing obsession with AI, interest in rapidly developing research such as BERT, GPT-2, and an interest from an early age as motivations. Example quotes include `@teknium` saying "*I was obssessed with AI since stable diffusion and then even moreso when chatgpt came out*" and `@coffeebean6887` mentioning how "*Bert got me pretty excited with how everything was building on itself and all the nlp task benchmarks were getting broken and people kept putting out great research on this stuff*”.

• Career fears and challenges: @jaisel expressed concerns about career saturation and the presence of more capable individuals in the field. The general sentiment was that one should pursue genuine interests regardless of these fears, as @teknium suggested "If you find something else more interesting you should pursue it instead".
• Career paths and motivations: Users continued to discuss their career paths and motivations, with @max_paperclips mentioning a long-standing childhood interest in AI as a driving factor, and @felixultimaforeverromanempire sharing their work and educational journey focused on AI. An important point raised was the need for financial motivation in AI, as reflected in @felixultimaforeverromanempire's advice, "the impetus about AI is making money."
• Video links shared: @pradeep1148 and @jaisel shared a couple of YouTube videos. The videos' content was not specifically discussed in the messages. - video 1 - video 2

▷ #benchmarks-log (9 messages):

    - There are performances of multiple **AI models** shared, for different systems with various configurations:

- hf-causal-experimental (pretrained=deepseek-ai/deepseek-llm-67b-base,use_accelerate=True), with different tasks and metrics. The results seem varied depending on the tasks and they tested it against multiple tasks, like boolq, arc_challenge, arc_easy, openbookqa, winogrande etc. They also shared the benchmarks for the bigbench tasks like bigbench_causal_judgement, bigbench_disambiguation_qa, etc. - Performance results for AGIEval and GPT4ALL are posted by @teknium for Deepseek 67b and Qwen 72b. 'Deepseek 67b' scored 74.87 on GPT4ALL and 36.83 on AGIEval; 'Qwen 72b' scored 72.89 on GPT4ALL and 42.74 on AgieEval. - @tokenbender explains the areas of expertise required for understanding different models and tasks, such as: - Hellaswag for sentence completion ability, - ARC for reasoning and nuance, - MMLU for world knowledge and problem-solving, - MT Bench for multi turn chat. - They also mentioned the need to understand AGIEval and GPT4ALL. - The idea of creating a bot for post-processing the performances suggested by @gabriel_syme. - @teknium furthermore suggests creating a finetune that creates a new form of MT_Bench, effectively setting up an automated chatbot arena for judging two models against each other.

▷ #interesting-links (14 messages):

    - **StableLM Model Size Discussion**: `@teknium` asked whether stableLM is 2.6b or 3.6b. `@atgctg` provided a [Google Doc link](https://docs.google.com/document/d/1tza0OIdTZNNjTqhkWZLRC9ha9Sp7lumGF5ytthx_Ozw/edit) and `@euclaise` mentioned that Hugging Face states it as **2.8B**.

• Improvements in LLaMA: @metaldragon01 shared a Reddit post discussing improvements in LLaMA, with @main.ai commenting on the peculiar double transpose in their attention implementation.
• NVIDIA's Superchip for Recommenders and GNNs: @lightningralf posted a blog post about NVIDIA's new superchip for Recommenders and GNNs.
• Adept AI's Open Demos: @makya commented on Adept AI opening up sign-ups for demos and experiments.
• Vector Quantization Discussion: @danielpgonzalez commented on his first-time reading about vector quantization and how he appreciated the topic being shared.
• Impact of Elo Weirdness on LLM Rankings: @cs2716 posted a Twitter link discussing how Elo weirdness affects LLM rankings.
• Interesting About GPT-4: @tsunemoto shared an interesting Twitter post about GPT-4.

▷ #general (171 messages🔥):

    - **Benchmarks**: `@teknium` highlighted the benchmarks of **Hermes 2.5 vs Hermes 2 Performance**. They explained that bigbench and agieval were used because of their use in the orca paper. Truthfulqa was chosen since it runs faster and is on the hf leaderboard. [Discussion Link](https://discord.com/channels/general)

• Model Evaluations: Users showed interest in evaluating the performance of new models like qwen 72b and deepseek 67b. @teknium shared initial comparative benchmark data between qwen 72b vs Deepseek 67b, revealing that they perform better on different benchmark sets. @makya offered to evaluate these models against llama-2 70b. Discussion Link
• New Dataset: @gabriel_syme shared a new dataset called HelpSteer from NVIDIA and suggested its potential use for DPO. @tokenbender agreed, also noting its potential use for teaching good data overall due to the provided extra labels. The dataset can be found here
• Multi-Language Model Usage: @r3muxd asked about the feasibility of using two language models simultaneously. @euclaise and @vatsadev confirmed it is technically possible but would need the same tokenizer and sufficient VRAM. Discussion Link
• Managing OpenAI Compute: Users discussed the suspected scale of OpenAI's compute power, noting rumors of a 150k h100 cluster purchase by Meta and the potential for a GPT-4 scale model. They mused about the potential for using this computational power and the supposed cost. Discussion Link

▷ #ask-about-llms (83 messages):

    - **Training Decoder Only Models**: `@erogol` shared a [paper](https://arxiv.org/pdf/2203.16634.pdf) reporting that training decoder only models without position encoders works fine and asked if anyone had experience with this.

• PC Hardware Discussion: @max_paperclips inquired about a prebuilt PC for running machine learning models, specifically highlighting the Ryzen 7 and GeForce RTX 4090 hardware. @ufghfigchv assured that the Ryzen 7's performance is comparable to an Intel i9, and both agreed that AMD GPUs are problematic due to lack of universal support.
• PC Hardware Suggestions: In response to @max_paperclips' request for prebuilt PCs, @giftedgummybee shared a link to a PC with an i9 processor and suggested adding more RAM as necessary.
• OpenHermes 2.5 Finetuning: @spaceman777 asked for comments or experiences with the OpenHermes-2.5-Mistral-7B-16k model hosted on Hugging Face.
• Using GPU in LM Studio: @felixultimaforeverromanempire asked how to enable GPU usage in LM Studio, and @teknium advised checking the GPU checkbox and entering 9999 for the value.
• Axolotl Mistral Full Finetuning Config: In a discussion about finetuning Mistral with Axolotl, @erikrichardlarson provided a link to a full finetune example config.

▷ #collective-cognition (2 messages):

    - **Confusion regarding Project Names**: `@yobibyte` clarified their previous mentions of a certain project, correcting it to **Open Access AI Collective** also responsible for **Jackalope**.

• Project Ownership: @teknium confirmed that these projects are owned by user <@257999024458563585>.

▷ #memes (1 messages):

nonameusr: the suffering

LangChain AI Discord Summary

• Advice sought on LangChain Agents, with specific queries on using AgentExecutorIterator for JSON input processing during project planning stages (from @gitmo joe) and the efficacy of using CHAT_CONVERSATIONAL_REACT_DESCRIPTION Agent for web searches via the serp API, which has displayed poor response to complex queries (from @sid.pocketmail).
• Discussion on the effectiveness of fine-tuning a model vs using LangChain for conducting a progressive conversation raised by @baller.1337.
• Inquiry on choosing an embedding model for a chatbot + RAG system, with @jungle_jo questioning the column weights in the leaderboard.
• Solicitation for methods on importing large data sets for Q/A purposes into OpenAI API from @bobi_99349, with the data being PDFs stored in .bson format, and discussion on using Elastic Search for extraction.
• Community projects and resources shared included a career pathway at www.smartcareer.ai (via @shving90), the Jupyter AI project presented at AWS re:Invent with the recorded presentation (by @fortybus and @3coins), the hypertion Project with its GitHub repository (shared by @synacktra), a YouTube tutorial providing a basic introduction on OpenGPTs from LangChain (from @datasciencebasics), and the OpenML Guide featuring open-source and free resources on AI learning (@severus_27).
• General technical query on customizing the run name without using LLMChain and ChatOpenAI.generate (by @abhagsain).
• A reminder on debugging the chain through the server, suggesting first to debug the chain itself prior to writing unit tests / integration tests for the chain (@veryboldbagel).

LangChain AI Channel Summaries

▷ #general (19 messages):

    - **Selecting LangChain Agent**: `@gitmo joe` is seeking advice on which LangChain agent is best suited to process input from a json file during planning stage, and specifically inquired about the potential use of **AgentExecutorIterator**.

• Fine-tuning vs Using LangChain: @baller.1337 posed a question about whether it would be more effective to fine-tune a model or use LangChain for conducting a progressive conversation (collecting user's name, age, etc).
• Issues with CHAT_CONVERSATIONAL_REACT_DESCRIPTION Agent: @sid.pocketmail reported problems with using the CHAT_CONVERSATIONAL_REACT_DESCRIPTION agent for web searches via the serp api. The agent seems to fail at delivering accurate search results for complex queries.
• Choosing Embedding Models: @jungle_jo asked for advice on choosing an embedding model for a chatbot + RAG system, focusing on reciting book information. The user was particularly interested in knowing which column should have the most weight in this leaderboard.
• Importing Data for Q/A: @bobi_99349 sought advice on importing a large amount of document files into OpenAI API for question and answering purposes. The data is currently stored as PDFs in .bson format, and using Elastic Search for extraction was estimated to take a couple of days.

▷ #langserve (1 messages):

veryboldbagel: I don't use vscode much, so can't help with setting it up -- but perhaps anyone else here that's familiar with vscode could help? Also if you can I would suggest first trying to debug the chain itself instead of debugging the chain through the server -- the same approach can be useful for writing unit tests / integration tests for the chain that'll be exposed by the server

▷ #langchain-templates (1 messages):

abhagsain: <@1072591948499664996> How to customize the name of the run without using LLMChain. I'm simply using ChatOpenAI.generate

▷ #share-your-work (5 messages):

    - **AI Smart Career Start**: `@shving90` shared a link to a career defining pathway from [www.smartcareer.ai](https://www.smartcareer.ai/early-access/get-started) which provides opportunities in smart career advancement.

• Jupyter AI Presentation at AWS re:Invent: @fortybus and his teammate @3coins presented a project named Jupyter AI, built on LangChain, at AWS re:Invent. The recording of the presentation can be found here.
• hypertion Project: @synacktra introduced a project named hypertion and shared the GitHub repository link. The project aids in schema creation and invocation based on a function's signature or metadata.
• OpenGPTs from LangChain for Complete Beginners: @datasciencebasics shared a YouTube link that provides a first look at OpenGPTs from LangChain and is suitable for complete beginners.
• OpenML Guide: @severus_27 shared the OpenML Guide, a resource that embraces open-source and free resources and offers a wealth of information related to AI advancements and learning resources.

▷ #tutorials (1 messages):

    datasciencebasics: OpenGPTs FROM LangChain 🦜🔗 | FIRST LOOK | FOR Complete Beginners

Latent Space Discord Summary

• Participants shared useful resources related to AI coding: - @slono linked the OpenAI Code Review Prompt, discussing the reconciliation of various self aspects. ("Code Review Prompt") - @coffeebean6887 pointed out Emad's activity on Reddit, providing a link to a significant comment. ("Emad's Comment")
• The searchability of the name 'GPT Assistant' was questioned by user @slono.
• Language translation service discussion unfolded, with users @mitchellbwright and @mitch3x3 expressing frustration with LangChain, and @fanahova suggesting Semantic Kernel as a potential alternative.
• There were technical discussions around hardware optimizations, with @mitch3x3 and @coffeebean6887 speculating on the worthiness of using NVLink for running dual 3090s.
• A dialogue on AI language models took place; users @sarav1n and @slono discussed the need for having a standard specification for running various types of Language Models (LM) across different programming environments.
• In the #[llm-paper-club] channel, @eugeneyan shared a thread of AI-related academic papers, providing valuable resource for those interested in current research trends. ("Paper Thread")

Latent Space Channel Summaries

▷ #ai-general-chat (22 messages):

    - **Code Review Prompt by OpenAI**: User `@slono` shared the link to a code review prompt and commented about reconciling different aspects of self. 
- [Code Review Prompt](https://chat.openai.com/share/0a318748-5849-42d8-aff4-5b2d90303674)

• Emad on Reddit: User @coffeebean6887 noted Emad's activity on Reddit and provided a link to a specific comment. - Reddit User Emad - Emad's Comment
• Name of GPT Assistant: @slono expressed that 'GPT assistant' as a name for the chatbot is difficult to google for.
• Alternatives to LangChain: Users @mitchellbwright and @mitch3x3 noted frustrations with LangChain, and @fanahova suggested Semantic Kernel as an alternative.
• NVLink for 3090s: Users @mitch3x3 and @coffeebean6887 discussed if NVLink is worth it for running dual 3090s. @coffeebean6887 suggested to skip it unless building a bigger rig and stressed the importance of running them at pcie 4.0 x8.
• Standard Spec for Language Model (LM): Users @sarav1n and @slono discussed the necessity of a standard specification for running different types of LMs across various programming languages.

▷ #llm-paper-club (1 messages):

eugeneyan: here’s the first dozen or so papers in thread form: https://x.com/eugeneyan/status/1670271775337480193

Alignment Lab AI Discord Summary

• Admin availability request by @zolandinho in general chat, prompting a brief conversation within the guild.
• Extended discussion on the status of project launches and publishing models led by @ufghfigchv, with other participants in the discussion.
• Speculation and inquiry around possible future token considerations, spurred by @bdog1741, with multiple guild members engaged in the conversation.
• @lightningralf comments on the cryptocurrency bull run predicting an extreme scenario in the next year to a year and a half.
• Concerns over the quality of the 'no_robots' Dataset brought up by @imonenext with comparisons to alternative solutions such as OASST, without any clear consensus built in the conversation.
• @turgutluk observes notable performance improvements with CRLFT Ablations when specified name replacements are implemented, intending to conduct human evaluations for a more comprehensive comparison with other models.
• @giftedgummybee shares a link to the lang-uk dataset on Hugging Face, proposing a collective interest in re-augmenting this dataset with GPT-4.

Alignment Lab AI Channel Summaries

▷ #general-chat (15 messages):

    - **Admin Availability Request**: `@zolandinho` asked if any admin was available to answer questions.

• Project's Status Discussion: The user @ufghfigchv clarified that they have been working and publishing models for months in response to a launch query.
• Token Inquiry: @bdog1741 speculated that @zolandinho was asking about a possible token, and desired clarity on whether this would be a consideration in the future. There is uncertainty among participants, including @autometa and @ufghfigchv, regarding what this token might entail.
• Cryptocurrency Bull Run: @lightningralf remarked on the rising trend of requesting tokens due to a slow-starting cryptocurrency bull run and predicted an extreme scenario in the coming 12 to 18 months.
• Quality of the 'no_robots' Dataset: @imonenext inquired about the https://huggingface.co/datasets/HuggingFaceH4/no_robots dataset and if it was high-quality following a complaint about its quality. Comparison with OASST is also mentioned but no clear consensus is reached.

▷ #oo (5 messages):

    - **CRLFT Ablations Performance**: `@turgutluk` shared that CRLFT, upon replacing "user" and "assistant" with "gpt4-user" and "gpt4-assistant" respectively, appears to improve performance according to the GPT-Eval template they defined. Human evaluation will be conducted for comparison with the top-2 models.

• Lang-UK Dataset Reaugmentation: @giftedgummybee shared a link to the lang-uk dataset on Hugging Face, and queried if anyone is interested in reaugmenting the dataset with GPT-4.

LLM Perf Enthusiasts AI Discord Summary

• Mozilla-Ocho's Llamafile Github Repository is gaining interest within the community. User @pantsforbirds shared the repository link in the #opensource channel, classifying it as extremely promising. The mention was further validated by @thisisnotawill who found out about llamafile from a tweet.
• User @thisisnotawill expressed a need for an alternative to gpt 3.5 that is less censored for sending prompts. This is not specific for local use but for testing purposes.
• A shared Google Document was highlighted by @joshcho_ in the #general channel, affirming it had received positive feedback on Twitter.
• Due to insufficient information, event details and confirmation of an unspecified news from the #irl and #openai channels could not be included in this summary.

LLM Perf Enthusiasts AI Channel Summaries

▷ #general (2 messages):

• @joshcho_ shared a Google Document of interest, and mentioned that it was also shared on Twitter and praised the document.

▷ #opensource (4 messages):

    - **Mozilla-Ocho's Llamofile Github Repo**: User `@pantsforbirds` shared a link to a Github repository, [llamafile](https://github.com/Mozilla-Ocho/llamafile), and found it *extremely promising*.

• Discussion of Local vs Remote AI Testing Needs: User @thisisnotawill commented that he noticed llamafile via a tweet. He expressed his need for an alternative to gpt 3.5 that is less censored for sending prompts, not necessarily for local use but just for testing purposes.

▷ #irl (1 messages):

frandecam: Im down- wheres the event?

▷ #openai (1 messages):

dongdong0755: https://x.com/rowancheung/status/1730444618666320128?s=20 so confirmed?

Skunkworks AI Discord Summary

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MLOps @Chipro Discord Summary

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YAIG (a16z Infra) Discord Summary

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