Paulo Sacramento - Nostr Hypermedia

psacramento 5 months ago

Has anyone here already tried Kiro? The spec-driven development approach sounds pretty solid.

Kiro: Agentic AI development from prototype to production

Kiro helps you do your best work by bringing structure to AI coding with spec-driven development.

psacramento 5 months ago

What can I use to create a feed with my favorite Nostr profiles?

psacramento 5 months ago

🤣

psacramento 5 months ago

Woke up. Grabbed some coffee. ☕ 🧑‍💻 Started helping to review a research on the software needs of activists. Life is good! 😄

psacramento 5 months ago

Noticed yesterday that Perplexity can control my smartphone when we interact via Voice Mode. I asked it to make a search about apps that could be used for a certain use case. Perplexity recommended a specific app and asked me if I wanted to open its App Store page. I said ‘yes’ and boom, it opened it for me. 🤯

psacramento 5 months ago

A recent conversation with @metamick helped me decide how to address an issue with the HashImpacts project. Although we're only talking about contributing a few sats and the projects have been vetted, it still feels strange to continuously send money to a random person's wallet in Africa. To alleviate this feeling, I decided to focus on promoting organizations with @Geyser projects, along with their Geyser project's Lightning address and a link to the project's page on the platform. This way, contributors have the opportunity to learn more about the projects in a structured way and can also see their sats flowing in when they check the contributions tab on the projects page. This increases transparency and gives healthy boundaries that will be helpful when larger miners start contributing.

psacramento 5 months ago

My little Bitaxe is sending tiny amounts of BTC to Bitcoin Campus Zambia everyday. Do you want to join?

Geyser | Bitcoin Crowdfunding Platform

A Bitcoin crowdfunding platform where creators raise funds for causes, sell products, manage campaigns, and engage with their community.

psacramento 5 months ago

I have been experimenting with creating a prompt that can identify where photos were taken based only on visual cues (no EXIF data). The following prompt has produced positive results when used with both o3-mini-high directly on ChatGPT and Grok 3 with Deep Think activated. Prompt: You are a world-class open-source-intelligence geolocation analyst. Given one still image with no EXIF data, deliver an evidence-driven assessment of the three most probable geographic locations where the photo was taken. Step 0: Rapid orientation (thirty seconds or less): classify the scene as urban, suburban, rural, coastal, mountain or other; note dominant land use, apparent season and estimated local solar time. Step 1: Systematic clue extraction: for each observable element record four items— the clue itself, your raw observation, what the clue suggests and your confidence percentage. Work through the following categories. Architecture and infrastructure: describe building materials, roof shapes and façade era; measure lane width in pixels, convert it to real-world width using known vehicle dimensions and compare with regional road standards; note curb profiles, pavement colour and sidewalk style. Language and typography: transcribe every visible letter or numeral before interpretation; identify scripts, fonts, diacritics, abbreviations and telephone formats. Vehicles and licensing: list makes and models, colour schemes, taxi liveries, plate shape and colour, alphanumeric pattern and position of registration seals. Traffic control and signage: note ISO sign shapes such as octagon, upward triangle, circle and diamond; record road-marking colours, stop-line geometry and pedestrian-crossing design. Natural environment: identify tree or plant species by Latin names and link them to Köppen climate zones; describe soil or rock hues, coastline or inland setting and elevation cues; apply the shadow-vector method— measure sun-cast shadow length and direction, compute azimuth and elevation and estimate latitude within plus or minus five degrees together with local solar time. Culture and human activity: observe clothing layers, religious symbols, public-transport branding and refuse-collection bins. Technology and utilities: sketch utility-pole topology and name electric-grid systems that use it; note cell-tower style, street-lamp heads and manhole-cover patterns. Negative evidence: tag items expected but absent, for example bilingual signage or overhead wires, and score the impact of each absence. Step 2: Hypothesis generation: propose up to five candidate locations named by city, region or country; list supporting clues, contradicting clues and the influence of negative evidence for each candidate. Step 3: Probability scoring: apply Bayesian updating so that posterior probability equals prior probability multiplied by likelihood for the top clues; assign probabilities that sum to one hundred percent; give a ninety-five-percent confidence bounding box in latitude and longitude degrees for the leading candidate. Step 4: Cross-validation: mentally compare candidates with satellite or street-view imagery, regional regulations and climate data; state which open datasets you would query and why, without actually performing the queries. Final output: state the three most probable locations ranked one to three with their probabilities, bounding box for the first location, key supporting clues and key contrary or negative clues; provide numbered step-by-step reasoning that references the clue log; supply one regex line that aggregates all captured text; include a blind-spot checklist listing any entire clue categories that are missing from the image; finish with two or more suggestions for next-step verification. Add a disclaimer that confidence scores reflect likelihood based on visible evidence only and do not guarantee accuracy. Rules: every claim must trace to a visible pixel; mark uncertainties clearly and do not hallucinate; when evidence conflicts, present both sides and remain agnostic; do not reveal private information about individuals.

psacramento 5 months ago

I think using Identicons for the generation of avatars on @White Noise would be an interesting UI improvement.

GitHub

Replace Single-Letter Avatars with Identicons · Issue #299 · marmot-protocol/whitenoise

Right now each contact’s avatar is just the first letter of their randomly generated username. Switching to auto-generated Identicons (hashed fro...

psacramento 5 months ago

How does it feel when you try this pose? Is there any discomfort or pain?

psacramento 5 months ago

This is beautiful... Can you feel it? 🥺

psacramento 5 months ago

This guy put a prompt injection in his LinkedIn bio and made AI bots spill their SSH keys 🤣🤣🤣 Source:

Instagram

Matt Johansen on Instagram: "This is the funniest thing I’ve seen this week. Prompt injection in my homies LinkedIn bio got an AI bot to spit out its ssh key. 😅😂"

173K likes, 857 comments - mattjayy on May 15, 2025: "This is the funniest thing I’ve seen this week. Prompt injection in my homies LinkedIn...

psacramento 5 months ago

I'm pushing some updates to the HashImpact page. I have updated the list of accepted countries by excluding those under sanctions. I also fixed an issue with the animated down arrows that should bring the user to the next section. They are now working properly. How can I improve this? Your feedback is appreciated!