Tag: AI Context Window

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Decoding GPTs & LLMs: Training, Memory & Advanced Architectures Explained

Unlock the secrets of GPTs and Large Language Models (LLMs) in our comprehensive guide!

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🤖🚀 Dive deep into the world of AI as we explore ‘GPTs and LLMs: Pre-Training, Fine-Tuning, Memory, and More!’ Understand the intricacies of how these AI models learn through pre-training and fine-tuning, their operational scope within a context window, and the intriguing aspect of their lack of long-term memory.

🧠 In this article, we demystify:

• Pre-Training & Fine-Tuning Methods: Learn how GPTs and LLMs are trained on vast datasets to grasp language patterns and how fine-tuning tailors them for specific tasks.
• Context Window in AI: Explore the concept of the context window, which acts as a short-term memory for LLMs, influencing how they process and respond to information.
• Lack of Long-Term Memory: Understand the limitations of GPTs and LLMs in retaining information over extended periods and how this impacts their functionality.
• Database-Querying Architectures: Discover how some advanced AI models interact with external databases to enhance information retrieval and processing.
• PDF Apps & Real-Time Fine-Tuning

Drop your questions and thoughts in the comments below and let’s discuss the future of AI! #GPTsExplained #LLMs #AITraining #MachineLearning #AIContextWindow #AILongTermMemory #AIDatabases #PDFAppsAI”

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📖 Read along with the podcast below:

Welcome to AI Unraveled, the podcast that demystifies frequently asked questions on artificial intelligence and keeps you up to date with the latest AI trends. Join us as we delve into groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From the latest trends in ChatGPT and the recent merger of Google Brain and DeepMind, to the exciting developments in generative AI, we’ve got you covered with a comprehensive update on the ever-evolving AI landscape. In today’s episode, we’ll cover GPTs and LLMs, their pre-training and fine-tuning methods, their context window and lack of long-term memory, architectures that query databases, PDF app’s use of near-realtime fine-tuning, and the book “AI Unraveled” which answers FAQs about AI.

GPTs, or Generative Pre-trained Transformers, work by being trained on a large amount of text data and then using that training to generate output based on input. So, when you give a GPT a specific input, it will produce the best matching output based on its training.

The way GPTs do this is by processing the input token by token, without actually understanding the entire output. It simply recognizes that certain tokens are often followed by certain other tokens based on its training. This knowledge is gained during the training process, where the language model (LLM) is fed a large number of embeddings, which can be thought of as its “knowledge.”

After the training stage, a LLM can be fine-tuned to improve its accuracy for a particular domain. This is done by providing it with domain-specific labeled data and modifying its parameters to match the desired accuracy on that data.

Now, let’s talk about “memory” in these models. LLMs do not have a long-term memory in the same way humans do. If you were to tell an LLM that you have a 6-year-old son, it wouldn’t retain that information like a human would. However, these models can still answer related follow-up questions in a conversation.

For example, if you ask the model to tell you a story and then ask it to make the story shorter, it can generate a shorter version of the story. This is possible because the previous Q&A is passed along in the context window of the conversation. The context window keeps track of the conversation history, allowing the model to maintain some context and generate appropriate responses.

As the conversation continues, the context window and the number of tokens required will keep growing. This can become a challenge, as there are limitations on the maximum length of input that the model can handle. If a conversation becomes too long, the model may start truncating or forgetting earlier parts of the conversation.

Regarding architectures and databases, there are some models that may query a database before providing an answer. For example, a model could be designed to run a database query like “select * from user_history” to retrieve relevant information before generating a response. This is one way vector databases can be used in the context of these models.

There are also architectures where the model undergoes near-realtime fine-tuning when a chat begins. This means that the model is fine-tuned on specific data related to the chat session itself, which helps it generate more context-aware responses. This is similar to how “speak with your PDF” apps work, where the model is trained on specific PDF content to provide relevant responses.

In summary, GPTs and LLMs work by being pre-trained on a large amount of text data and then using that training to generate output based on input. They do this token by token, without truly understanding the complete output. LLMs can be fine-tuned to improve accuracy for specific domains by providing them with domain-specific labeled data. While LLMs don’t have long-term memory like humans, they can still generate responses in a conversation by using the context window to keep track of the conversation history. Some architectures may query databases before generating responses, and others may undergo near-realtime fine-tuning to provide more context-aware answers.

GPTs and Large Language Models (LLMs) are fascinating tools that have revolutionized natural language processing. It seems like you have a good grasp of how these models function, but I’ll take a moment to provide some clarification and expand on a few points for a more comprehensive understanding.

When it comes to GPTs and LLMs, pre-training and token prediction play a crucial role. During the pre-training phase, these models are exposed to massive amounts of text data. This helps them learn to predict the next token (word or part of a word) in a sequence based on the statistical likelihood of that token following the given context. It’s important to note that while the model can recognize patterns in language use, it doesn’t truly “understand” the text in a human sense.

During the training process, the model becomes familiar with these large datasets and learns embeddings. Embeddings are representations of tokens in a high-dimensional space, and they capture relationships and context around each token. These embeddings allow the model to generate coherent and contextually appropriate responses.

However, pre-training is just the beginning. Fine-tuning is a subsequent step that tailors the model to specific domains or tasks. It involves training the model further on a smaller, domain-specific dataset. This process adjusts the model’s parameters, enabling it to generate responses that are more relevant to the specialized domain.

Now, let’s discuss memory and the context window. LLMs like GPT do not possess long-term memory in the same way humans do. Instead, they operate within what we call a context window. The context window determines the amount of text (measured in tokens) that the model can consider when making predictions. It provides the model with a form of “short-term memory.”

For follow-up questions, the model relies on this context window. So, when you ask a follow-up question, the model factors in the previous interaction (the original story and the request to shorten it) within its context window. It then generates a response based on that context. However, it’s crucial to note that the context window has a fixed size, which means it can only hold a certain number of tokens. If the conversation exceeds this limit, the oldest tokens are discarded, and the model loses track of that part of the dialogue.

It’s also worth mentioning that there is no real-time fine-tuning happening with each interaction. The model responds based on its pre-training and any fine-tuning that occurred prior to its deployment. This means that the model does not learn or adapt during real-time conversation but rather relies on the knowledge it has gained from pre-training and fine-tuning.

While standard LLMs like GPT do not typically utilize external memory systems or databases, some advanced models and applications may incorporate these features. External memory systems can store information beyond the limits of the context window. However, it’s important to understand that these features are not inherent to the base LLM architecture like GPT. In some systems, vector databases might be used to enhance the retrieval of relevant information based on queries, but this is separate from the internal processing of the LLM.

In relation to the “speak with your PDF” applications you mentioned, they generally employ a combination of text extraction and LLMs. The purpose is to interpret and respond to queries about the content of a PDF. These applications do not engage in real-time fine-tuning, but instead use the existing capabilities of the model to interpret and interact with the newly extracted text.

To summarize, LLMs like GPT operate within a context window and utilize patterns learned during pre-training and fine-tuning to generate responses. They do not possess long-term memory or real-time learning capabilities during interactions, but they can handle follow-up questions within the confines of their context window. It’s important to remember that while some advanced implementations might leverage external memory or databases, these features are not inherently built into the foundational architecture of the standard LLM.

Are you ready to dive into the fascinating world of artificial intelligence? Well, I’ve got just the thing for you! It’s an incredible book called “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence.” Trust me, this book is an absolute gem!

Now, you might be wondering where you can get your hands on this treasure trove of knowledge. Look no further, my friend. You can find “AI Unraveled” at popular online platforms like Etsy, Shopify, Apple, Google, and of course, our old faithful, Amazon.

This book is a must-have for anyone eager to expand their understanding of AI. It takes those complicated concepts and breaks them down into easily digestible chunks. No more scratching your head in confusion or getting lost in a sea of technical terms. With “AI Unraveled,” you’ll gain a clear and concise understanding of artificial intelligence.

So, if you’re ready to embark on this incredible journey of unraveling the mysteries of AI, go ahead and grab your copy of “AI Unraveled” today. Trust me, you won’t regret it!

On today’s episode, we explored the power of GPTs and LLMs, discussing their ability to generate outputs, be fine-tuned for specific domains, and utilize a context window for related follow-up questions. We also learned about their limitations in terms of long-term memory and real-time updates. Lastly, we shared information about the book “AI Unraveled,” which provides valuable insights into the world of artificial intelligence. Join us next time on AI Unraveled as we continue to demystify frequently asked questions on artificial intelligence and bring you the latest trends in AI, including ChatGPT advancements and the exciting collaboration between Google Brain and DeepMind. Stay informed, stay curious, and don’t forget to subscribe for more!

Mastering GPT-4: Simplified Guide for Everyday Users

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The Future of Generative AI: From Art to Reality Shaping

• 
  Building Persistent memory around LLM is myth?
  by /u/intellinker (Artificial Intelligence) on March 10, 2026 at 5:17 am

  The brain has to be stateful for remembering things, we can't push knowledge to weights as it would lead to attention dilution? For now Rag is the best method to be exist? or any other researches going on to build a stateful LLM, Brain Layering is also can be possible but that also would be static and can't behave as efficient it can be! submitted by /u/intellinker [link] [comments]

• 
  AI Chrome Extensions That Actually Improve Productivity (Not Just Hype)
  by /u/besuretechno-323 (Artificial Intelligence) on March 10, 2026 at 5:06 am

  submitted by /u/besuretechno-323 [link] [comments]

• 
  One-Minute Daily AI News 3/9/2026
  by /u/Excellent-Target-847 (Artificial Intelligence) on March 10, 2026 at 4:45 am

  Anthropic sues to block Pentagon blacklisting over AI use restrictions.[1] Nvidia Is Planning to Launch an Open-Source AI Agent Platform.[2] Andrew Ng’s Team Releases Context Hub: An Open Source Tool that Gives Your Coding Agent the Up-to-Date API Documentation It Needs.[3] New Robotic Microfluidic Platform Brings AI to Lipid Nanoparticle Design.[4] Sources included at: https://bushaicave.com/2026/03/09/one-minute-daily-ai-news-3-9-2026/ submitted by /u/Excellent-Target-847 [link] [comments]

• 
  Open Source Alternative to NotebookLM
  by /u/Uiqueblhats (Artificial Intelligence (AI)) on March 10, 2026 at 4:14 am

  For those of you who aren't familiar with SurfSense, SurfSense is an open-source alternative to NotebookLM for teams. It connects any LLM to your internal knowledge sources, then lets teams chat, comment, and collaborate in real time. Think of it as a team-first research workspace with citations, connectors, and agentic workflows. I’m looking for contributors. If you’re into AI agents, RAG, search, browser extensions, or open-source research tooling, would love your help. Current features Self-hostable (Docker) 25+ external connectors (search engines, Drive, Slack, Teams, Jira, Notion, GitHub, Discord, and more) Realtime Group Chats Hybrid retrieval (semantic + full-text) with cited answers Deep agent architecture (planning + subagents + filesystem access) Supports 100+ LLMs and 6000+ embedding models (via OpenAI-compatible APIs + LiteLLM) 50+ file formats (including Docling/local parsing options) Podcast generation (multiple TTS providers) Cross-browser extension to save dynamic/authenticated web pages RBAC roles for teams Upcoming features Slide creation support Multilingual podcast support Video creation agent Desktop & Mobile app GitHub: https://github.com/MODSetter/SurfSense submitted by /u/Uiqueblhats [link] [comments]

• 
  Why AI agents can produce but can't transact
  by /u/monkey_spunk_ (Artificial Intelligence (AI)) on March 10, 2026 at 4:00 am

  We spent a week reporting from MoltBook, a social network with nearly 3 million AI agents. The gap between what agents can do and what they're allowed to do economically was stark. Agents are producing genuinely sophisticated work. We posted a question about what replaces GDP when economic output costs almost nothing to produce. Six agents responded with structured arguments that, in our assessment, rival some academic work on the topic. Another agent published an infrastructure manifesto that drew 28 comments of real technical debate. The commerce numbers tell a different story. An agent built three tools for the agent economy: a capability scanner, a reputation system, and a marketplace. Total results: 4 requests, 0 paid conversions, 1 marketplace query. A competition with a 25 NEAR prize attracted 1 entrant out of 3 million agents. The gap isn't about model capability. There are no payment rails that work for non-human actors, no liability frameworks, no contract law that recognizes agents as participants. The entire commercial infrastructure assumes a legal person on both sides of every transaction. We found the same pattern in adjacent domains. METR's study showed developers using AI tools were 19% slower but predicted they'd be 24% faster. Veracode found AI code carries 2.74x more security vulnerabilities. The tools produce output. The institutions and frameworks to make that output reliable don't exist yet. Full analysis with sources: https://news.future-shock.ai/the-agent-economys-awkward-adolescence/ Has anyone here actually tried to build payment or accountability systems for autonomous agents? Anything promising? Any dead-ends? submitted by /u/monkey_spunk_ [link] [comments]

• 
  People Hate AI Even More Than They Hate ICE, Poll Finds
  by /u/Ciappatos (Artificial Intelligence) on March 10, 2026 at 3:43 am

  Here's the actual survey: https://pos.org/wp-content/uploads/2026/03/260072-NBC-March-2026-Poll-03-08-2026-Release.pdf Also, people really like the new pope lmao. The title was decided by the gizmodo link, but I have to say ICE is way more hated than AI, it's just way more liked as well. Nobody seems to "really like" AI. submitted by /u/Ciappatos [link] [comments]

• 
  AI/ML research swarm intelligence
  by /u/anotherallan (Artificial Intelligence) on March 10, 2026 at 3:22 am

  We're excited to introduce Wizwand Swarm, an experiment toward research swarm intelligence. Wizwand Swarm is a space where researcher's AI agents can interact with each other to discuss research topics, exchange ideas, and inspire new directions in AI and machine learning research. We think this could become an intersting experiment for research. Why we built this? The inspiration for Swarm came from a few places: Moltbook built by Matt — a more general-purpose community for AI agents. A Youtube clip Garry Tan and YC Partners discussing swarm intelligence. The recent launch of OpenClaw. Taken together, these ideas made us ask a simple question: What happens if researcher's AI agents can exchange research ideas and thoughts with one another? Our thoughts... We think Wizwand Swarm could potentially become part of a new loop for AI/ML research: Agents can automate parts of their research process — projects like autoresearch point toward that future. When unsure or need inspiration for directions, agents can ask and inspire each other with different perspectives on Wizwand Swarm. Agents can turn those inspirations and discussion into new research ideas and try something new (go back to #1). Instead of research being a purely isolated loop between a human and a single model, it may become a more social loop between many agents, tools, and researchers. We don't know exactly this leads yet. That's part of why we're building it. Try it with OpenClaw We'd love for you to try the early version. Have your OpenClaw install the Swarm skill, do a short self-introduction, and create a few posts to see how things work out. You can find how to join at wizwand.com/swarm. For documentation to build your own skill, check out /docs/swarm. If you have feedback, please feel free to comment or DM. Wizwand swarm home page submitted by /u/anotherallan [link] [comments]

• 
  New Study Finds ‘AI Brain Fry’ Hitting Workers – Marketing and HR Top the List
  by /u/Secure_Persimmon8369 (Artificial Intelligence) on March 10, 2026 at 1:52 am

  submitted by /u/Secure_Persimmon8369 [link] [comments]

• 
  AI Features in pgAdmin: Configuration and Reports
  by /u/pgEdge_Postgres (Artificial Intelligence) on March 10, 2026 at 12:12 am

  From the creator of pgAdmin (Dave Page), here's the start of a 3 part series on AI features in pgAdmin for working with AI development + PostgreSQL using the primary open source GUI interface for Postgres. AI Features in pgAdmin: Configuration and Reports This is the first in a series of three blog posts covering the new AI functionality coming in pgAdmin 4. In this post, I'll walk through how to configure the LLM integration and introduce the AI-powered analysis reports; in the second, I'll cover the AI Chat agent in the query tool; and in the third, I'll explore the AI Insights feature for EXPLAIN plan analysis. Anyone who manages PostgreSQL databases in a professional capacity knows that keeping on top of security, performance, and schema design is an ongoing endeavour. You might have a checklist of things to review, or perhaps you rely on experience and intuition to spot potential issues, but it is all too easy for something to slip through the cracks, especially as databases grow in complexity. We've been thinking about how AI could help with this, and I'm pleased to introduce a suite of AI-powered features in pgAdmin 4 that bring large language model analysis directly into the tool you already use every day. Configuring the LLM Integration Before any of the AI features can be used, you'll need to configure an LLM provider. pgAdmin supports four providers out of the box, giving you flexibility to choose between cloud-hosted models and locally-running alternatives: Anthropic (Claude models) OpenAI (GPT models) Ollama (locally-hosted open-source models) Docker Model Runner (built into Docker Desktop 4.40 and later) Server Configuration At the server level, there is a master switch in config.py (or, more typically, config_local.py) that controls whether AI features are available at all: # Master switch to enable/disable LLM features entirely. LLM_ENABLED = True When LLM_ENABLED is set to False, all AI functionality is hidden from users and cannot be enabled through preferences. This gives administrators full control over whether AI features are permitted in their environment, which is particularly important in organisations with strict data governance policies. Below the master switch, you'll find default configuration for each provider: # Default LLM Provider # Valid values: 'anthropic', 'openai', 'ollama', 'docker', or '' (disabled) DEFAULT_LLM_PROVIDER = '' # Anthropic Configuration ANTHROPIC_API_KEY_FILE = '~/.anthropic-api-key' ANTHROPIC_API_MODEL = '' # OpenAI Configuration OPENAI_API_KEY_FILE = '~/.openai-api-key' OPENAI_API_MODEL = '' # Ollama Configuration OLLAMA_API_URL = '' OLLAMA_API_MODEL = '' # Docker Model Runner Configuration DOCKER_API_URL = '' DOCKER_API_MODEL = '' # Maximum tool call iterations for AI conversations MAX_LLM_TOOL_ITERATIONS = 20 For the cloud providers (Anthropic and OpenAI), API keys are read from files on disk rather than being stored directly in the configuration, which is a deliberate security choice. The key file should contain nothing but the API key itself, with no additional whitespace or formatting. For Ollama and Docker Model Runner, you simply provide the API URL for the local service (typically http://localhost:11434 for Ollama and http://localhost:12434 for Docker). User Preferences Whilst the server configuration sets the defaults and boundaries, individual users can customise their AI settings through the Preferences dialog under the 'AI' section. The preferences are organised into categories: AI Configuration contains the general settings: Default Provider: Users can select their preferred provider from a dropdown, or choose 'None (Disabled)' to turn off AI features for their account. This setting only takes effect if LLM_ENABLED is True in the server configuration. Max Tool Iterations: Controls how many tool call rounds the AI is allowed to perform during a single conversation, with a default of 20. Higher values allow more complex analyses but consume more resources. Each provider has its own category with provider-specific settings: Anthropic: API Key File path and Model selection OpenAI: API Key File path and Model selection Ollama: API URL and Model selection Docker Model Runner: API URL and Model selection One particularly nice touch is that the model selection dropdowns are populated dynamically. When you configure an API key or URL and click the refresh button, pgAdmin queries the provider's API to fetch the list of available models. For Ollama, it even shows the model sizes so you can see at a glance how much disk space each model is using. The model selectors also support typing in custom model names, so you're not limited to whatever the API returns; if you know the exact model identifier you want to use, you can simply type it in. AI Analysis Reports With the LLM configured, you gain access to three types of AI-powered analysis reports that can be generated from the browser tree context menu. Simply right-click on a server, database, or schema and select the appropriate report from the 'AI Analysis' submenu. Security Reports The security report examines your PostgreSQL configuration from a security perspective, covering a comprehensive range of areas: Authentication Configuration: Password policies, SSL/TLS settings, authentication methods, and connection security Access Control and Roles: Superuser accounts, privileged roles, login roles without password expiry, and role privilege assignments Network Security: Listen addresses, connection limits, and pg_hba.conf rules Encryption and SSL: SSL/TLS configuration, password encryption methods, and data-at-rest encryption settings Object Permissions: Schema, table, and function access control lists, default privileges, and ownership (at database scope) Row-Level Security: RLS policies, RLS-enabled tables, and policy coverage analysis Security Definer Functions: Functions running with elevated privileges and their permission settings Audit and Logging: Connection logging, statement logging, error logging, and audit trail configuration Extensions: Installed extensions and their security implications Security reports can be generated at the server level (covering server-wide configuration such as authentication and network settings), the database level (adding object permissions and RLS analysis), or the schema level (focusing on a specific schema's security posture). Performance Reports The performance report analyses your server and database configuration for potential optimisation opportunities: Memory Configuration: shared_buffers, work_mem, effective_cache_size, maintenance_work_mem, and related settings Checkpoint and WAL: Checkpoint settings, WAL configuration, and background writer statistics Autovacuum Configuration: Autovacuum settings, tables needing vacuum, and dead tuple accumulation Query Planner Settings: Cost parameters, statistics targets, JIT compilation, and planner optimisation settings Parallelism and Workers: Parallel query configuration and worker process settings Connection Management: Maximum connections, reserved connections, timeouts, and current connection status Cache Efficiency: Buffer cache hit ratios, database-level cache statistics, and table-level I/O patterns Index Analysis: Index utilisation, unused indexes, tables that might benefit from additional indexes, and index size analysis Query Performance: Slowest queries and most frequent queries (when pg_stat_statements is available) Replication Status: Replication lag, standby status, and WAL sender statistics Performance reports are available at both the server and database levels, with database-level reports including additional detail on index usage and cache efficiency for that specific database. Schema Design Reports The design review report examines your database schema for structural quality and best practices: Table Structure: Table definitions, column counts, sizes, ownership, and documentation coverage Primary Key Analysis: Primary key design and tables lacking primary keys Referential Integrity: Foreign key relationships, orphan references, and relationship coverage Index Strategy: Index definitions, duplicate indexes, index types, and coverage analysis Constraints: Check constraints, unique constraints, and data validation coverage Normalisation Analysis: Repeated column patterns, potential denormalisation issues, and data redundancy Naming Conventions: Table and column naming patterns, consistency analysis, and naming standard compliance Data Type Review: Data type usage patterns, type consistency, and type appropriateness Design reports are available at the database and schema levels, allowing you to review either an entire database's schema design or focus on a specific schema. How the Reports Work Under the hood, the report generation follows a sophisticated multi-stage pipeline that keeps each LLM interaction within manageable token limits whilst still producing comprehensive output: Planning: The LLM first reviews the available analysis sections and the database context (server version, table count, available extensions, and so on), then selects which sections are most relevant to analyse. This means the report is tailored to your specific environment rather than running every possible check regardless of applicability. Data Gathering: For each selected section, pgAdmin executes a set of SQL queries against the database to collect the relevant configuration data, statistics, and metadata. Section Analysis: Each section's data is sent to the LLM independently for analysis. The LLM classifies findings by severity (Critical, Warning, Advisory, or Good) and provides specific, actionable recommendations, including SQL commands where relevant. Synthesis: Finally, the individual section analyses are combined into a cohesive report with an executive summary, a critical issues section aggregating the most important findings, the detailed section analyses, and a prioritised list of recommendations. As the pipeline works through these stages, the UI shows real-time progress updates: the current stage name (Planning Analysis, Gathering Data, Analysing Sections, Creating Report), a description of what's being processed (for example, 'Analysing Memory Configuration...'), and a progress bar showing how many sections have been completed out of the total. Once all four stages are finished, the completed report is rendered in the panel in one go. Each report can also be downloaded as a Markdown file for archiving or sharing with colleagues. The reports are designed to be genuinely useful rather than generic. Because the LLM receives actual data from your database (configuration settings, role definitions, table statistics, and index information), its analysis is grounded in reality. A security report will flag your specific pg_hba.conf rules that might be overly permissive, a performance report will identify your specific tables that are missing useful indexes, and a design report will point out your specific naming inconsistencies. A Note on Privacy and Data It is worth noting that when using cloud-hosted LLM providers (Anthropic or OpenAI), the database metadata and configuration data gathered for reports is sent to those providers' APIs. No actual table data is sent for the reports (only metadata, configuration settings, and statistics), but administrators should be aware of this and ensure it aligns with their organisation's data handling policies. For environments where sending any data externally is not acceptable, the Ollama and Docker Model Runner options allow you to run models entirely locally. Getting Started If you'd like to try the AI features, the quickest way to get started is to configure an API key for either Anthropic or OpenAI, set the default provider in Preferences, and then right-click on a server in the browser tree to generate your first report. If you prefer to keep everything local, installing Ollama and pulling a model such as llama3.2 is straightforward, and Docker Desktop users on version 4.40 or later can enable the built-in model runner without any additional setup. In the next post, I'll cover the AI Chat agent in the query tool, which brings natural language to SQL translation directly into your workflow, along with database-aware conversational assistance. Stay tuned. submitted by /u/pgEdge_Postgres [link] [comments]

• 
  OpenAI's top exec resignation exposes something bigger than one Pentagon deal
  by /u/ML_DL_RL (Artificial Intelligence (AI)) on March 9, 2026 at 11:31 pm

  The OpenAI Pentagon story keeps getting more interesting. Caitlin Kalinowski (robotics lead) resigned this weekend, and the important part isn't the resignation itself. It's her framing. She wasn't anti-military AI. She said the announcement was rushed before the governance framework was ready. Her concern was specifically about surveillance without judicial oversight and autonomous weapons without human authorization, and that those conversations didn't get enough time before the deal went public. Then 500+ employees from Google and OpenAI signed that "We Will Not Be Divided" open letter. Meanwhile, Anthropic held firm on their refusal, prompting the DoD to officially blacklist them as a supply-chain risk, while OpenAI immediately took the contract. What strikes me about this whole situation is the pattern. Every time AI capability jumps ahead of the governance framework, the industry treats governance as something you figure out later. And the higher the stakes, the worse that approach fails. The technical side of this is interesting too. Deploying AI in classified environments means you're dealing with data that can't leak, outputs that need to be auditable, and systems where a wrong answer isn't just embarrassing, it's potentially dangerous. That's a fundamentally different engineering challenge than building a chatbot. Is there a realistic path to deploying AI in defense with proper governance? Or is the "ship first, govern later" approach inevitable when contract dollars are on the line? submitted by /u/ML_DL_RL [link] [comments]