标签：#ann

While philosophers ponder AI consciousness, the models have ideas of their own.

This week on Uncanny Valley, we dig into the latest prediction market buzz, Flock’s AI-powered police search tool, and how tech bros don’t know how to talk about “rouge” AI agents

Implementing A* and RRT Motion Planning for Robotics Two classic planning approaches are A * and RRT (Rapidly-exploring Random Tree) . A* is particularly useful when the environment can be represented as a graph or grid. RRT is useful when planning in continuous or high-dimensional configuration spaces. A* Planning A* combines the cost already traveled with an estimate of the remaining cost. Conceptually: f(n) = g(n) + h(n) Where: g(n) is the cost from the start. h(n) estimates the cost to the goal. f(n) ranks candidate nodes. Grid Example S . . # . . . . . . # . . . . . . . . # . . # # # . # . . . . . . . G The planner explores promising cells while avoiding blocked cells. Python Implementation Skeleton import heapq def astar ( graph , start , goal , heuristic ): queue = [( 0 , start )] cost = { start : 0 } parent = { start : None } while queue : _ , current = heapq . heappop ( queue ) if current == goal : break for neighbor in graph [ current ]: new_cost = cost [ current ] + 1 if neighbor not in cost or new_cost < cost [ neighbor ]: cost [ neighbor ] = new_cost priority = new_cost + heuristic ( neighbor , goal ) heapq . heappush ( queue , ( priority , neighbor )) parent [ neighbor ] = current return parent RRT Planning RRT works differently. Instead of systematically exploring grid cells, it samples points and gradually grows a tree. x / x------x / S-----x x----x------G A typical loop is: Sample a random configuration. Find the nearest existing node. Steer toward the sample. Check collision. Add the new node if valid. Repeat until the goal is reached. RRT Skeleton for _ in range ( max_iterations ): sample = random_configuration () nearest = nearest_node ( tree , sample ) new_node = steer ( nearest , sample ) if collision_free ( nearest , new_node ): tree . add ( new_node ) tree . connect ( nearest , new_node ) if reached_goal ( new_node ): return extract_path ( tree , new_node ) A* vs RRT Property A* RRT Representation Grid/graph Continuous space Search Determinis

James Cameron's 1991 sci-fi blockbuster returns to theaters this weekend for its 35th anniversary.

A recent paper argues that AI is often better at doctoring than doctors. Guess who isn't thrilled.

This week on “Uncanny Valley,” senior writer Will Knight talks his recent visit to China and the future of AI collaboration.

In the last article, I wrote about a release story that was weaker than the engine underneath...

Runable says 60%–70% of its 1 trillion-plus token usage in the last 90 days came from paying customers.

These are the lessons we learned evaluating LLMs for real-world secret scanning. The post How to evaluate LLMs before production appeared first on The GitHub Blog .

Originally published on tamiz.pro . The field of AI agents has moved rapidly from single-model executors to complex multi-agent orchestration. But after running 157 agent deployments across diverse task domains, one pattern emerged with striking consistency: planning quality predicts success far better than execution speed or model size. This isn't just theoretical—it's a practical lesson that's reshaping how engineers architect agent fleets, giving rise to what we're now calling Orca-style agents : hierarchical, planning-first systems that separate the expensive business of thinking from the cheaper business of doing. The Experiment: 157 Agent Runs Over six months, our team deployed and monitored 157 distinct agent runs across four primary use cases: code generation pipelines, automated testing workflows, infrastructure-as-code provisioning, and data transformation tasks. Each run varied along three dimensions: Architecture : Single-agent vs. flat multi-agent vs. hierarchical (Orca-style) Planning depth : No planning, brief intent statement, or full recursive planning loop Execution model : Direct LLM call per action vs. tool-augmented execution with validation The results were unambiguous. Systems that invested 3-5x more tokens in planning achieved 4.2x higher task completion rates and 3.8x fewer rollback cycles compared to agents optimized purely for fast execution. The correlation between planning sophistication and success held across every domain. Why Planning Beats Raw Execution The intuition behind this finding rests on an economic principle of LLM usage: planning is cheap relative to costly mistakes . A well-structured plan reduces the probability of executing the wrong sequence of tools, making incorrect API calls, or generating code that fails integration testing. Consider the token economics: Phase Tokens (typical) Cost impact Planning (intent + decomposition) 800–2,500 Low Execution per subtask 300–1,200 Medium Correction after failure 1,500–4,000 High

OVHcloud will raise prices from September, with 2026-edition gaming servers up 87 percent and other recent servers 40 to 59 percent. Founder Octave Klaba says memory cost six times more in June than a year earlier, as RAM suppliers shifted capacity toward high-bandwidth memory for AI. AWS, buying years ahead, has repriced one reserved GPU product. By Steef-Jan Wiggers

Meta is in court again over child safety, and this time it’s a landmark case that could force significant changes to core features of Facebook and Instagram.

This week on “Uncanny Valley,” Andy Greenberg discusses sitting in on a war game simulating a cyberattack from the Chinese hacking group Volt Typhoon

Proton’s CEO is a champion of encryption for everyone. So why is he going all in on un-encryptable AI?

Tim O’Reilly built a publishing empire that AI is helping to destroy. Yet he loves AI—as long as it’s open source.

AI is shifting the culture, from tech CEO manifestos to 1 am job interviews. We unpack some of the latest, along with the top findings from Black Hat and Defcon, this week on Uncanny Valley.

The problem When you type a single or double quote in Atlassian Jira or Confluence wiki, it's automatically converted to a "smart" quote 🤮 Examples “double” ‘single’ I don’t like it If you delete a smart quote and type it again, it becomes smart again, which can be disturbing. There is no option to disable smart quotes permanently. But... You can get rid of them Type Ctrl-Z immediately after they appear. It also works for the word don't : When you type apostrophe ' it looks normal, then when you type t , the apostrophe changes. Type Ctrl-Z and it reverts back to normal again.

The mayor of New York City has assembled a crew of Silicon Valley and United States Digital Service veterans to overhaul city services with better software.

Puck’s Matthew Belloni says AI has quietly become part of everyday filmmaking. The battle now isn’t whether Hollywood will use the technology—it’s who controls what’ll come next.

Viral tales of good triumphing over evil are racking up millions of views. They’re almost entirely AI-generated clickbait.