The Problem is Prompt Debt
Teams create prompt debt when they hand-tune prompts. They turn natural-language instructions into fragile specs, spend more time adjusting wording, and tie the application to one model... read more
Updates and recent posts about GPT-5.4.. Link
Link
@kala shared a link, 2 days, 10 hours ago
FAUN.dev()
Cisco Bets On WideField Security Acquisition To Tackle Agentic AI Security Gap
Cisco executives plan to acquire WideField Security so Cisco teams can add identity and session telemetry to agentic AI security operations... read more
Link
@kala shared a link, 2 days, 10 hours ago
FAUN.dev()
AI's Affordability Crisis
The AI platforms are running the drug-dealer's algorithm, with subsidies resulting in overwhelming demand for their products. Estimates show that the cost of generating tokens ranges from $8 to $14 to generate $1 in revenue. Companies transitioning to token-based pricing have seen significant increa.. read more
Link
@kala shared a link, 2 days, 10 hours ago
FAUN.dev()
Model Size Scaling in 2023-2031
Token generation speed is constrained by the speed at which the relevant HBM can be read, depending on model size and pipeline setup. Model sizes feasible for each year between 2023 and 2031 range from 10T in 2026 to 1.4 quadrillion in 2031, with pretraining compute and HBM specifications playing es.. read more
Link
@devopslinks shared a link, 2 days, 10 hours ago
FAUN.dev()
How we migrated a live routing system using AI-assisted refactoring
Datadog says engineers can use AI-assisted refactoring for critical migrations when they own the data model and use tests to limit each rewrite to one method. Engineers should keep system design and optimization in human hands... read more
Link
@devopslinks shared a link, 2 days, 10 hours ago
FAUN.dev()
The memory crisis is getting so bad that even retro RAM prices are going to the Moon
DRAM buyers are switching to DDR2 as suppliers run short of mainstream memory, and some device makers are redesigning products around the legacy chip... read more
Link
@devopslinks shared a link, 2 days, 10 hours ago
FAUN.dev()
The database that refused to die: How Postgres survived its own creators
Postgres was developed by Michael Stonebraker and based on the Ingres database system, following ideas from Ted Codd for relational databases. Postgres introduced support for abstract data types, leading to its widespread adoption and compatibility with modern cloud infrastructure. Despite its succe.. read more
Link
@devopslinks shared a link, 2 days, 10 hours ago
FAUN.dev()
Some notes on Lambda MicroVMs
AWS would give developers a middle tier between Lambda functions and ECS or EC2. You get per-VM identity, network addressability, and stronger process isolation while keeping Lambda's burst model. The runtime would fit workloads that need warm state, longer-lived workers, per-tenant sandboxes, proto.. read more
Link
@devopslinks shared a link, 2 days, 10 hours ago
FAUN.dev()
Local AI for Penetration Testing & Research
Model intelligence and tradecraft have progressed a lot in the year that's passed since I last tried something similar. There's a lot of hype around the research Anthropic is publishing; however, cost and privacy are still problems. When there's no guarantee that a thorough job was performed, this t.. read more
Link
@devopslinks shared a link, 2 days, 10 hours ago
FAUN.dev()
.self: A new Top-Level Domain built from theground up to support self-hosting
Operated as a public good, .self TLD will be designed and implemented according to human-centered principles to enable anyone to take full ownership of their data... read more
One of the defining features of GPT-5.4 is its ability to operate as part of agent-style workflows. The model can interact with tools, APIs, and external systems to complete tasks that extend beyond simple text generation. It also introduces native computer-use capabilities, allowing AI agents to operate applications using keyboard and mouse commands, screenshots, and browser automation frameworks such as Playwright.
GPT-5.4 supports context windows of up to one million tokens, enabling it to process and reason over very large documents, long conversations, or complex project contexts. This makes it suitable for tasks such as analyzing codebases, generating technical documentation, working with large spreadsheets, or coordinating long-running workflows. The model also introduces a feature called tool search, which allows it to dynamically retrieve tool definitions only when needed. This reduces token usage and makes it more efficient to work with large ecosystems of tools, including environments with dozens of APIs or MCP servers.
In addition to improved reasoning and automation capabilities, GPT-5.4 focuses on real-world productivity tasks. It performs better at generating and editing spreadsheets, presentations, and documents, and it is designed to maintain stronger context across longer reasoning processes. The model also improves factual accuracy and reduces hallucinations compared with previous versions.
GPT-5.4 is available across OpenAI’s ecosystem, including ChatGPT, the OpenAI API, and Codex. A higher-performance variant, GPT-5.4 Pro, is also available for users and developers who require maximum performance for complex tasks such as advanced research, large-scale automation, and demanding engineering workflows. Together, these capabilities position GPT-5.4 as a model aimed not just at conversation, but at executing real work across software systems.
