#code intelligence

[secret-spirit]

This is what i tend to interpret in my head with these pairings

[cheekios]

Rationing Insulin.

Blood sugar reading this morning. The average blood sugar reading should be between 60mg/dl - 100mg/dl. I am terrified of not being able to administer my insulin simply because I was too poor to afford it. I am strongly in need of community help.

CA: $HushEmu

PP: emuslayter@gmail.com

I am happy to announce I raised $33 🎉 I only need $417 to get my prescription

[abyssyby]

but but but zayne in a barbie princess & the pauper au where’s he’s JULIANNNN AAAAAGHHH and he’s your private tutor & he’s so invested in teaching you about sciences & literature and is so utterly besotted with the stars in your eyes when you express your thoughts and opinions on a matter.

and he’s your dearest friend, your most trusted partner. your heart flutters when he speaks, even until your eyes roll to the back of your head when sometimes science gets boring and all there is is his beautiful, lulling voice. and he’s always hesitant to wake you, brushes his fingers over your cheek ever so slightly, just enough to feel the warmth of your skin. content in seeing you rest before him.

he knows every aspect about you, everything that makes you swoon and smile. and he loves using that to his advantage— spoiling you in ways that a person in his position can.

accompanying you to hidden excursions outside the palace, showing you places he frequents that you cannot go to because of your protections and duties to the crown.

sneaking you sweets and wildflowers from over the wall. borrowing you books from the town’s library you’ve never seen within your own palace’s. leaving fresh bread from the bakery in the square on your desk or bedroom doorstep.

and you love him. you’ve loved him since, you love him now. but when you’re betrothed to be with another, to be with a king he steps back. he restrains himself from the chemistry that grows between you behind closed doors. keeps his distance. hesitates to touch you. sometimes to even look at you for too long, robs himself of his favorite constellations in your eyes, lest he do something he’ll regret.

he is still a brilliant tutor, a kind hearted and gentle soul— but you cannot help but feel that you’re beginning to lose your best friend.

“but I am not a king. I cannot give you what he can.”

[saantyp]

Que te elijan es el mejor acto de amor que puede suceder.

[rgrrrlsstuff]

"I visualize a time when we will be to robots what dogs are to humans, and I’m rooting for the machines." —Claude Shannon

[lorenzonuti]

Tolerance threshold.

[tangramkey]

i love my Basketbot Portal AU

[wire-bunch]

i think there should be more robots that arent good at programming and dont know a lot about related subjects honestly. i mean humans dont have an in depth understanding of biology and anatomy just because theyre human, sure they know what kind of stuff is supposed to be inside of them but not very specific biological processes, names of proteins and all that stuff - so why should a robot know what each little part of it does? if its purpose isnt to be a self-repairing mechanic, whats the point of knowing where all the individual little wires connect to and what each of them is responsible for? let robots be a bit dumber is what im saying

[miss-conjayniality]

they would’ve been best friends.

[itsriotmotherfuckers]

Sit down when I tell you this because some of you are dying on this hill and it’s just wrong.

Harry Potter is, under no circumstances, the Regulus Black of the Lightning Bolt Era.

[ananke-xiii]

everybody saying "poor jim", "jim's the smart one", "jim's the real hero", nobody saying that he was pivotal in that moment because his mother was a piano teacher who taught him the importance of music as a universal language. in a show where the mothers are the "chosen ones", the real heroes who are, however, never believed, called "crazy" and mistreated/killed (but they're getting their payback). let's all sit with that for a moment and then rethink about what we write.

[tangentiallly]

One way to spot patterns is to show AI models millions of labelled examples. This method requires humans to painstakingly label all this data so they can be analysed by computers. Without them, the algorithms that underpin self-driving cars or facial recognition remain blind. They cannot learn patterns.

The algorithms built in this way now augment or stand in for human judgement in areas as varied as medicine, criminal justice, social welfare and mortgage and loan decisions. Generative AI, the latest iteration of AI software, can create words, code and images. This has transformed them into creative assistants, helping teachers, financial advisers, lawyers, artists and programmers to co-create original works.

To build AI, Silicon Valley’s most illustrious companies are fighting over the limited talent of computer scientists in their backyard, paying hundreds of thousands of dollars to a newly minted Ph.D. But to train and deploy them using real-world data, these same companies have turned to the likes of Sama, and their veritable armies of low-wage workers with basic digital literacy, but no stable employment.

Sama isn’t the only service of its kind globally. Start-ups such as Scale AI, Appen, Hive Micro, iMerit and Mighty AI (now owned by Uber), and more traditional IT companies such as Accenture and Wipro are all part of this growing industry estimated to be worth $17bn by 2030.

Because of the sheer volume of data that AI companies need to be labelled, most start-ups outsource their services to lower-income countries where hundreds of workers like Ian and Benja are paid to sift and interpret data that trains AI systems.

Displaced Syrian doctors train medical software that helps diagnose prostate cancer in Britain. Out-of-work college graduates in recession-hit Venezuela categorize fashion products for e-commerce sites. Impoverished women in Kolkata’s Metiabruz, a poor Muslim neighbourhood, have labelled voice clips for Amazon’s Echo speaker. Their work couches a badly kept secret about so-called artificial intelligence systems – that the technology does not ‘learn’ independently, and it needs humans, millions of them, to power it. Data workers are the invaluable human links in the global AI supply chain.

This workforce is largely fragmented, and made up of the most precarious workers in society: disadvantaged youth, women with dependents, minorities, migrants and refugees. The stated goal of AI companies and the outsourcers they work with is to include these communities in the digital revolution, giving them stable and ethical employment despite their precarity. Yet, as I came to discover, data workers are as precarious as factory workers, their labour is largely ghost work and they remain an undervalued bedrock of the AI industry.

As this community emerges from the shadows, journalists and academics are beginning to understand how these globally dispersed workers impact our daily lives: the wildly popular content generated by AI chatbots like ChatGPT, the content we scroll through on TikTok, Instagram and YouTube, the items we browse when shopping online, the vehicles we drive, even the food we eat, it’s all sorted, labelled and categorized with the help of data workers.

Milagros Miceli, an Argentinian researcher based in Berlin, studies the ethnography of data work in the developing world. When she started out, she couldn’t find anything about the lived experience of AI labourers, nothing about who these people actually were and what their work was like. ‘As a sociologist, I felt it was a big gap,’ she says. ‘There are few who are putting a face to those people: who are they and how do they do their jobs, what do their work practices involve? And what are the labour conditions that they are subject to?’

Miceli was right – it was hard to find a company that would allow me access to its data labourers with minimal interference. Secrecy is often written into their contracts in the form of non-disclosure agreements that forbid direct contact with clients and public disclosure of clients’ names. This is usually imposed by clients rather than the outsourcing companies. For instance, Facebook-owner Meta, who is a client of Sama, asks workers to sign a non-disclosure agreement. Often, workers may not even know who their client is, what type of algorithmic system they are working on, or what their counterparts in other parts of the world are paid for the same job.

The arrangements of a company like Sama – low wages, secrecy, extraction of labour from vulnerable communities – is veered towards inequality. After all, this is ultimately affordable labour. Providing employment to minorities and slum youth may be empowering and uplifting to a point, but these workers are also comparatively inexpensive, with almost no relative bargaining power, leverage or resources to rebel.

Even the objective of data-labelling work felt extractive: it trains AI systems, which will eventually replace the very humans doing the training. But of the dozens of workers I spoke to over the course of two years, not one was aware of the implications of training their replacements, that they were being paid to hasten their own obsolescence.

— Madhumita Murgia, Code Dependent: Living in the Shadow of AI

[detective-juliet-ohara]

if lassie was a dog which breed do you think he'd be?

I have never considered that question before in my life but it was... surprisingly easy to come up with an answer: Belgian Malinois!

They're intense herding dogs but are also incredibly loyal and form deep, long-lasting bonds with their owners. They're also very intelligent and athletic and are the most common breed trained to be police dogs!

In fact, Benny, our newest K-9, has been at the SBPD a lot recently and the similarities between her and Carlton are uncanny.

They both love to follow me around everywhere, get scratches behind their ears, and yell at squirrels. They're also both house-trained.

[technologywhis]

Oh yes — that’s the legendary CIA Triad in cybersecurity. It’s not about spies, but about the three core principles of keeping information secure. Let’s break it down with some flair:

⸻

1. Confidentiality

Goal: Keep data private — away from unauthorized eyes.

Think of it like locking away secrets in a vault. Only the right people should have the keys.

Examples:

• Encryption

• Access controls

• Two-factor authentication (2FA)

• Data classification

Threats to it:

• Data breaches

• Shoulder surfing

• Insider threats

⸻

2. Integrity

Goal: Ensure data is accurate and trustworthy.

No tampering, no unauthorized changes — the data you see is exactly how it was meant to be.

Examples:

• Checksums & hashes

• Digital signatures

• Version control

• Audit logs

Threats to it:

• Malware modifying files

• Man-in-the-middle attacks

• Corrupted files from system failures

⸻

3. Availability

Goal: Data and systems are accessible when needed.

No point in having perfect data if you can’t get to it, right?

Examples:

• Redundant systems

• Backup power & data

• Load balancing

• DDoS mitigation tools

Threats to it:

• Denial-of-service (DoS/DDoS) attacks

• Natural disasters

• Hardware failure

⸻

Why it matters?

Every cybersecurity policy, tool, and defense strategy is (or should be) built to support the CIA Triad. If any one of these pillars breaks, your system’s security is toast.

Want to see how the CIA Triad applies to real-world hacking cases or a breakdown of how you’d protect a small business network using the Triad? I got you — just say the word.

[vickypersch]

Rip Sodapop Curtis you would have loved spellcheck and grammarly 🙂‍↕️