Yeni XBOX lideri Asha Sharma, marka için büyük adımlar atmaya devam ediyor

Yeni XBOX lideri Asha Sharma, marka için büyük adımlar atmaya devam ediyor

Yeni XBOX lideri Asha Sharma, marka için büyük adımlar atmaya devam ediyorYeni XBOX lideri Asha Sharma, marka için büyük adımlar atmaya devam ediyor. Göreve kısa süre önce başlamış olmasına rağmen ses getiren çok sayıda değişikliğe imza atan Sharma, yönetim kademesini de güçlendiriyor. Son güncelleme kapsamında ünlü analist ve eski Amazon Studios yöneticisi “Matthew Ball” Stratejiden Sorumlu Başkan (CSO) olarak göreve başlarken, şirkete eski Azure OpenAI ve AI Core yöneticisi “Scott Vliet” ise Teknolojiden Sorumlu Başkan (CTO) olarak katılıyor. Ayrıca Xbox’ın iş birlikleri ve iş geliştirme kurumsal başkan yardımcılığı görevine “Chris Schnakenberg” getirilmiş durumda. Schnakenberg, 2024 yılında firmaya katılmadan önce Microsoft’un 2023 yılında satın aldığı Activision Blizzard bünyesinde 12 yıldan fazla bir süre görev yapmış. Sharma, özellikle dikkat çeken Matthew Ball için yaptığı açıklamada tecrübeli ismin oyun, medya ve teknoloji dünyasında büyük bir saygınlığa sahip olduğunu ve göreve başladığı günden bu yana kendisine danışmanlık yapan kıdemli bir oyuncu olduğunu belirtti. Ball özellikle, Sharma’nın “sektördeki herkesin mutlaka okuması gerekiyor” şeklinde nitelendirdiği yıllık “The […]

Yeni XBOX lideri Asha Sharma, marka için büyük adımlar atmaya devam ediyor yazısı ilk olarak Teknoloji haberleri – LOG üzerinde yayınlandı.

Why Prompt Engineering Is Just an Expensive Way to Be Incompetent

Watching some people “use AI at work” has convinced me of one thing:

AI is not replacing competent developers any time soon.

But it is doing a fantastic job of exposing who didn’t really know what they were doing in the first place.

You’ve probably seen this pattern already:

  • 50 prompts.
  • 20 rewrites.
  • Endless “make it more robust / more scalable / more production-ready” messages.
  • Zero understanding of what’s actually happening.

Meanwhile, a real developer could have:

  • understood the problem,
  • written a small, clear solution,
  • and finished in one or two focused passes.

Prompt engineering doesn’t magically turn non-technical people into builders. It mostly turns their confusion into very expensive text.

The Claude Credit Summary That Made Me Feel Weirdly Safe

Everyone talks about AI as an existential threat.

Then you open your team’s or company’s usage dashboard and see something like:

  • thousands of credits burned on trivial tasks,
  • long chat sessions where the same vague question is rephrased 20 times,
  • reports and docs generated, then manually rewritten for hours.

You realize:

The people most excited about “prompt engineering” are often the ones who never learned how to think like engineers.

Real examples companies are already sharing:

  • managers spending hours correcting AI-generated reports that “took 5 minutes,” creating what some call prompt engineering debt—a new layer of work on top of already busy jobs;
  • no-code builders burning hundreds of credits a day because they keep asking vague questions instead of clarifying requirements or reading basic docs;
  • teams running complex tasks on the most expensive models when cheaper ones (or a quick human fix) would have done the job.

Meanwhile, controlled studies keep saying the same thing:

  • AI coding assistants give big gains in some contexts,
  • and outright slow developers down in others—especially when tasks are small or the codebase is well understood.

If you need 30 prompts to get what a competent dev would produce in one shot, the problem isn’t that you need ‘better prompting techniques.’ The problem is you don’t know what you’re asking for.


“Prompt Engineering” vs Actual Engineering

Prompt engineering was briefly sold as a new job category:

  • no code,
  • no math,
  • no systems thinking,
  • just “talk well to the model.”

Companies are already walking that back.

The emerging consensus:

  • “prompt engineer” as a standalone role is fading;
  • what’s valuable now are people who understand systems, data, constraints, and verification—and happen to use AI well.
    In other words:

  • AI skills are a multiplier on real expertise,

  • not a replacement for the expertise.
    The difference in practice:

  • A non-technical “prompt engineer” burns tokens asking the model to invent APIs, architectures, and glue code from scratch.

  • A real developer feeds the model context, constraints, and code, then uses it to refactor, scaffold, or check specific parts.

Prompt engineering without domain knowledge is just creative ways to waste money faster.

The Hidden Inefficiency: Prompt Engineering Debt

People talk about technical debt.

We’re now accumulating prompt engineering debt:

  • AI-generated docs that sound authoritative but are subtly wrong,
  • half-baked scripts or UIs that “mostly work” but hide security or performance issues,
  • workflows no one can reproduce because the steps lived entirely inside chat logs.
    You save 10 minutes by letting AI draft something.

You lose:

  • hours verifying, correcting, and reformatting,
  • more hours when future you or someone else has to figure out “how this was made,”
  • trust, when stakeholders discover the tool hallucinated key details.

Managers and non-technical leaders are already reporting:

  • AI “helped” them finish something fast,
  • but they then spent several hours fixing it,
  • net result: more tired, not more productive.

This is the real cost nobody includes in their “AI saved me X hours!” posts.

AI doesn’t just create output. It creates obligations: to verify, to correct, to own the result. Prompt debt is still debt.

Watching a Prompt Power User Struggle With a Two-Minute Bug

The most painful demos of “AI superusers” often look like this:

  • They hit a bug or small logic issue.
  • Instead of reading the code or error message, they:
    • paste everything into Claude or another model,
    • ask it to “fix the problem,”
    • get a large diff,
    • apply it blindly,
    • break something else.

Repeat.

If you’re an actual developer, you know what a normal path looks like:

  • read the error,
  • follow the stack trace,
  • inspect inputs and outputs,
  • change one or two lines,
  • add a test if needed.

Two minutes.

The “prompt engineer” route:

  • 20+ minutes and 100+ credits,
  • three or four different attempts,
  • unsatisfying sense that “it works now but not sure why.”
    This is not AI’s fault.

This is what happens when someone:

  • doesn’t understand the system,
  • doesn’t trust themselves to reason about it,
  • and thinks more prompting is always the answer.

If AI is your first reflex for every tiny issue, you’re not a power user. You’re outsourcing your thinking.


Being an AI Superuser Is Not the Same as Being a Developer

There is such a thing as an AI power user.

They look nothing like the people throwing 400-line prompts at every problem.
Real power users:

  • have solid fundamentals in at least one domain (dev, data, ops, product),
  • use AI to compress workflows they already understand,
  • treat models as tools in a chain, not as all-knowing oracles.

They:

  • script repeated tasks,
  • integrate AI into editors, terminals, and CI pipelines,
  • design small, reliable loops where the model does the mechanical parts.

They do not:

  • spend all day rewriting the same vague prompt,
  • rely on AI to invent systems they don’t understand,
  • brag about number of tokens used as if that were a productivity metric.

Being good with AI is not about ‘talking to it a lot.’ It’s about knowing enough to ask for the right thing once.

The Ego Layer: Prompt Engineering as a Mask for Insecurity

Here’s the uncomfortable part:

For some people, “prompt engineering” is a socially acceptable way to hide insecurity.

Instead of saying:

  • “I don’t understand this system,”
  • “I need to learn the basics,”
  • “I’m out of my depth,”

they say:

  • “I’m working on AI workflows,”
  • “I’m refining the prompt,”
  • “The model just needs better instructions.”
    It sounds sophisticated.

It keeps them in the conversation.

It lets them avoid asking the “stupid questions” they really need to ask.

But underneath that layer of AI jargon, nothing changes:

  • no better mental models,
  • no better debugging skills,
  • no better understanding of trade-offs.

Prompt engineering becomes a costume you wear so people don’t see that you’re scared to admit what you don’t know.

How Skilled Developers Actually Use AI (and Why That’s Threat-Proof)

Most senior devs who integrate AI deeply share the same pattern:

They use AI to:

  • accelerate things they already know how to do,
  • generate first drafts they fully understand and can rewrite,
  • explore alternatives or edge cases they might have missed.

They don’t:

  • let AI design the architecture of a system they don’t understand,
  • push AI-generated changes without reading them carefully,
  • use AI as a shield from having to think.

Studies and field reports show:

  • AI assistants give big boosts when engineers know their stack and use AI as an exoskeleton;
  • in unfamiliar systems or without clear specs, they can slow things down or create new failure modes.

The job that’s safest long-term is still:

  • understanding systems,
  • making decisions under uncertainty,
  • reviewing and owning the output—human or AI-generated.

The thing that keeps you safe is not how many prompts you can write. It’s how many decisions you can own.

If You’re Technical: Why This Whole Trend Should Calm You Down

If you’re a real engineer worried about AI replacing you, look carefully at how most “prompt superusers” actually work today.

You’ll see:

  • a lot of noise,
  • a lot of wasted tokens,
  • a lot of shallow output,
  • and a lot of work created for the people who still have to be accountable.

The market is already correcting:

  • the hype around “prompt engineer” as a standalone role is fading;
  • companies are hiring for AI + X (engineering, data, product, security), not for “AI whisperers”;
  • senior developer roles are shifting toward more design, review, integration, and governance—not away from them.

AI will absolutely replace:

  • a chunk of low-skill work,
  • some tasks that used to justify junior roles,
  • a lot of fake productivity.

It will not replace:

  • clear thinking,
  • system-level understanding,
  • the ability to define what “good” looks like in the first place.

Your job is not to compete with people burning credits. Your job is to be the person who knows what should happen before anyone opens a chat window.

I fix the Angular apps that generalists break.

I’m Karol Modelski, senior Angular developer and frontend architect rescuing legacy B2B SaaS frontends.

If your Angular app is slowing your team down, start with a 3‑minute teardown of your current setup: https://www.karol-modelski.scale-sail.io/

Upgrading OpenBSD 7.8 to OpenBSD 7.9

The lastest version of OpenBSD has been released this week and announced on the Mailinglist (but also on X/Twitter by Brian Steele). As usual, lot of improvement, this publication is just a quick summary.

  • Read the release note and the upgrade procedure.

  • Ensure you have enough place on your partitions.

df -h
  • Be prepared to upgrade your database like PostgreSQL. Do a Backup.

  • Start with one device to see if everything is fine.

  • Upgrade with sysupgrade.

sysupgrade
  • After the reboot, upgrade the packages with pkg_add
pkg_add -uUra
  • Do a second reboot if necessary

  • Upgrade PostgreSQL and other services which have been upgraded.

  • Do a third reboot to check if all services are started correctly during boot (reboot proof servers)

And… It should be okay! No big deal for this upgrade on my side, everything worked without issues.

Cover Image by A Perry on Unsplash

We migrated a .NET app from ABP Framework to Granit without downtime – here’s the playbook

We just finished moving a 15-module .NET service from ABP Framework
to Granit — without a maintenance window, without
a code freeze, and without a parallel run-everything-twice phase. The whole
thing took about ten weeks running alongside feature work.

This post walks through the playbook: why we moved, the architecture that
made an incremental cutover possible, the one-page mapping between ABP
and Granit primitives, a real before/after of a CRUD module, and the
single technical gotcha that will trip up anyone whose ABP entities use
int or long keys.

If you want the full reference with every cross-cutting concern covered
(multi-tenancy, audit, settings, permissions, background jobs,
specifications), the canonical version lives in the
Granit migration guide. This
article is the field report.

Why we moved off ABP

Let me get this out of the way: ABP is a fine framework. We didn’t
move because something was broken. We moved because four trends in our
codebase finally added up:

  • We had been writing Minimal API endpoints by hand for new features for
    two years — CrudAppService<TEntity, TDto> had stopped earning its
    keep on anything that wasn’t pure CRUD.
  • Our DDD layering (Domain / Application.Contracts / Application /
    EntityFrameworkCore) was generating four projects per module and a
    lot of IObjectMapper boilerplate that nobody read.
  • We had adopted Wolverine for messaging and
    were fighting IDistributedEventBus to make it route the way we
    wanted.
  • New hires kept asking “why is there a parallel IStringLocalizer
    abstraction on top of the BCL one?” — and we never had a great answer.

Granit is the framework that crystallized out of those four answers: a
modular .NET 10 stack with native Wolverine, Minimal API as the
endpoint surface, BCL primitives where they suffice, and CQRS shaped as
the default rather than a workaround. The conceptual overlap with ABP
is enormous — same [DependsOn] module system, same IMultiTenant
interface name, same ISettingProvider interface name, same
AuditedEntity family — which is what made the migration tractable in
the first place.

The strangler-fig architecture

A big-bang rewrite was never on the table. We ran the new Granit host
alongside the existing ABP host, behind the same ingress, sharing the
same database and the same Keycloak issuer. The ingress decides
per-URL which host serves the request. Each module flips over
independently.

            ┌─────────────────┐
   browser ─►   Ingress (YARP)│
            └────────┬────────┘
                     │
        ┌────────────┴─────────────┐
        │                          │
   /api/legacy/*               /api/orders/*
        │                       /api/inventory/*
        ▼                          ▼
   ┌────────┐                 ┌─────────┐
   │  ABP   │                 │ Granit  │
   │  host  │                 │  host   │
   └───┬────┘                 └────┬────┘
       │                           │
       └────────┬──────────────────┘
                ▼
         ┌──────────────┐
         │ Shared DB    │
         │ Shared OIDC  │
         └──────────────┘

The shared database is the linchpin. Three rules:

  1. Schema ownership belongs to the host that wrote the table first.
    EF Core migrations are generated by that host; the other side either
    reads via a read-only DbContext or generates its own migrations
    into the same __EFMigrationsHistory table.
  2. Audit and tenant columns must keep the same shape during the
    cutover.
    ABP uses CreatorId / CreationTime; Granit uses
    CreatedBy / CreatedAt. We solved this with EF Core column
    overrides on the Granit side (HasColumnName("CreationTime")) so
    physical schema didn’t drift until the ABP host was gone.
  3. OIDC stays as-is. Tokens minted for the ABP host work as-is on
    the Granit host. No parallel user store, no token exchange, no
    “auth migration” sub-project.

The conceptual mapping (cheat sheet)

We carried this table around in the team Notion. The full version is
on the docs site;
here are the rows we hit most:

ABP primitive Granit equivalent
AbpModule + [DependsOn(...)] GranitModule + [DependsOn(...)]
IApplicationService / CrudAppService<...> A Minimal API endpoint class with handler methods
IRepository<TEntity, TKey> (~30 helpers) DbContext directly, or a thin custom repository
AuditedEntity<TKey> / FullAuditedEntity<TKey> AuditedEntity / FullAuditedEntity (Id is always Guid)
IMultiTenant + ICurrentTenant.Id IMultiTenant + ICurrentTenant.Id
ISettingProvider.GetOrNullAsync(name) ISettingProvider.GetOrNullAsync(name) (same shape)
IPermissionChecker + PermissionDefinitionProvider ASP.NET Core authorization policies (per-endpoint)
IDistributedEventBus.PublishAsync Wolverine bus.PublishAsync
IBackgroundJobManager.EnqueueAsync Wolverine bus.ScheduleAsync
ISpecification<T> / Specification<T> Granit.Persistence.Specification<T> + inline Spec.For<T>()
AuditLog table AuditEntry rows via GranitAuditingModule
IObjectMapper (AutoMapper) Mapperly (compile-time) or manual

The pattern in this table is the headline: most ABP primitives have a
direct Granit equivalent under the same name or a near-identical
shape
. The migration is rarely a re-architecture; it’s mostly
namespace changes plus a few opinionated rewrites where Granit picks a
different default.

The killer before/after: CrudAppService → Minimal API

This is the diff that drove the migration. Same module — Inventory,
a CRUD aggregate with audit, validation, and four permissions — in
both stacks.

ABP, the standard shape:

[Authorize(MyAppPermissions.Inventory.Default)]
public class InventoryAppService
    : CrudAppService<InventoryItem, InventoryItemDto, Guid,
                     PagedAndSortedResultRequestDto, CreateInventoryItemDto>,
      IInventoryAppService
{
    public InventoryAppService(IRepository<InventoryItem, Guid> repository)
        : base(repository)
    {
        GetPolicyName    = MyAppPermissions.Inventory.Default;
        GetListPolicyName = MyAppPermissions.Inventory.Default;
        CreatePolicyName = MyAppPermissions.Inventory.Create;
        UpdatePolicyName = MyAppPermissions.Inventory.Edit;
        DeletePolicyName = MyAppPermissions.Inventory.Delete;
    }
}

Plus an IApplicationService interface in *.Application.Contracts,
plus DTOs split across two projects, plus a PermissionDefinitionProvider
declaring the permission tree. Five REST endpoints auto-generated, but
also five points of indirection you cannot see in this file.

Granit, the explicit shape:

public static class InventoryEndpoints
{
    public static IEndpointRouteBuilder MapGranitInventory(
        this IEndpointRouteBuilder app)
    {
        var group = app.MapGroup("/inventory")
            .WithTags("Inventory")
            .RequireAuthorization("inventory.read");

        group.MapGet("/{id:guid}",   GetById);
        group.MapGet("/",            List);
        group.MapPost("/",           Create).RequireAuthorization("inventory.create");
        group.MapPut("/{id:guid}",   Update).RequireAuthorization("inventory.edit");
        group.MapDelete("/{id:guid}", Delete).RequireAuthorization("inventory.delete");

        return app;
    }

    public static async Task<Created<InventoryItemResponse>> Create(
        InventoryItemCreateRequest request,
        IInventoryRepository repo,
        ICurrentTenant currentTenant,
        CancellationToken ct)
    {
        var item = InventoryItem.Create(
            Guid.NewGuid(), currentTenant.Id,
            request.Name, request.Sku, request.Quantity, request.UnitPrice);

        await repo.AddAsync(item, ct);
        return TypedResults.Created($"/inventory/{item.Id}",
            new InventoryItemResponse(item.Id, item.Name, item.Sku,
                item.Quantity, item.UnitPrice, item.CreatedAt));
    }

    // GetById / List / Update / Delete follow the same pattern.
}

The trade-off is that you write each endpoint signature yourself —
there is no shortcut equivalent to CrudAppService, which is the whole
point of moving to explicit Minimal API. In return:

  • Number of projects per module: 4 → 1 (or 2 if you split persistence).
  • DTO ↔ entity mapping: IObjectMapper (runtime reflection) → static
    ToResponse method, or Mapperly (compile-time source generation).
  • Permissions: PermissionDefinitionProvider + [Authorize(name)]
    → ASP.NET Core policies declared in the module’s ConfigureServices.
  • Validation: data annotations on DTOs → FluentValidation classes,
    auto-discovered by AddGranitValidatorsFromAssemblyContaining<T>().
  • Endpoint registration: declarative (REST auto-exposed) → explicit
    (you call api.MapGranitInventory() from Program.cs). This is
    Granit’s convention across every shipped module — MapGranitAuditing(),
    MapGranitParties(), MapGranitBlobStorage(), etc.

You can read all five endpoint signatures in one sitting. That is the
point.

The one gotcha: Guid keys

Here is the single thing that will derail an ABP migration if you
discover it late: Granit’s Entity base type fixes Id as Guid.
There is no Entity<TKey>, no AuditedAggregateRoot<int>. The framework
opinion is that every entity has a Guid (v4 or v7) for distributed
id generation, sharding, and tenant isolation.

If your ABP entities use AggregateRoot<int> or <long>, you need a
one-shot key rewrite before any module is ported — both hosts have
to agree on the key type once they read and write the same rows.

We used the deterministic UUID v5 strategy in PostgreSQL: derive each
new Guid from the existing int via uuid_generate_v5(namespace,
id::text)
. The same input always yields the same UUID, so foreign
keys port via the same hash — no lookup table, no downtime.

-- Add new key column, backfilled with deterministic UUIDs.
ALTER TABLE inventory_items
  ADD COLUMN id_new uuid;

UPDATE inventory_items
  SET id_new = uuid_generate_v5('a0eebc99-9c0b-4ef8-bb6d-6bb9bd380a11', id::text);

-- Rewrite every dependent FK the same way (one statement per FK).
ALTER TABLE inventory_movements
  ADD COLUMN inventory_item_id_new uuid;

UPDATE inventory_movements
  SET inventory_item_id_new =
    uuid_generate_v5('a0eebc99-9c0b-4ef8-bb6d-6bb9bd380a11', inventory_item_id::text);

-- Swap the primary key.
ALTER TABLE inventory_items DROP CONSTRAINT inventory_items_pkey;
ALTER TABLE inventory_items DROP COLUMN id;
ALTER TABLE inventory_items RENAME COLUMN id_new TO id;
ALTER TABLE inventory_items ADD PRIMARY KEY (id);

-- Re-create each FK constraint.
ALTER TABLE inventory_movements DROP COLUMN inventory_item_id;
ALTER TABLE inventory_movements RENAME COLUMN inventory_item_id_new TO inventory_item_id;
ALTER TABLE inventory_movements
  ADD CONSTRAINT fk_inventory_movements_item
    FOREIGN KEY (inventory_item_id) REFERENCES inventory_items(id);

Critical sequencing: regenerate the ABP-side entity to Guid Id
before running the SQL. If the ABP host stays on int Id after the
rewrite, its EF Core model diverges from the physical schema and every
read throws. We discovered this during a staging dry-run and it would
have been a production incident.

The mapping-table alternative (random Guid per row, persisted in a
_key_migration table) is documented in the
full guide section.

The license footnote that wasn’t a footnote

I left this for last because it doesn’t fit the technical narrative, but
it ended up being the line-item that got the migration unblocked with
our legal team:

License What that means in practice
ABP Framework (OSS core, abpframework/abp) LGPL-3.0 You can link against it from proprietary code (it’s “Lesser” GPL). But modifications to the framework itself must be published under LGPL. Many enterprise SCA tools (Black Duck, FOSSA, GitHub Advanced Security) flag LGPL as “needs legal review” before merge.
ABP Commercial (Suite, LeptonX, premium modules) Proprietary, paid per-seat Vendor-tied, locked behind support contract.
Granit (entire OSS surface — framework, frontend, templates) Apache-2.0 Permissive. Use, modify, fork, redistribute — in proprietary derivatives too — with zero copyleft on your app code or your framework patches. Explicit patent grant. Auto-approved by most enterprise SCA pipelines.

Our SCA pipeline (FOSSA) was the unblocker. LGPL-3.0 was flagged as
“requires legal review” on every PR that touched a transitive ABP
dependency, generating a manual ticket each time. Apache-2.0 sails
through. That alone saved us a recurring 2-3 day legal cycle per
sprint, independent of the technical wins.

If you sit in a regulated industry (finance, health, government), the
SCA-friction argument may be the only one your CFO needs to hear.

What we did not migrate

Three things in our ABP setup did not have a 1:1 Granit equivalent.
Worth flagging upfront:

  • ABP Suite (codegen UI). Granit has no equivalent. We replaced
    Suite-generated module scaffolding with the dotnet new
    granit-microservice
    template and hand-written modules where the
    template wasn’t expressive enough.
  • The full generic-repository surface. IRepository<T, TKey>
    exposes ~30 helpers (GetPagedListAsync(skip, take, sorting),
    FirstOrDefaultAsync(predicate), …). Granit prefers DbSet<T>
    directly. We ported the four helpers we actually used as extension
    methods on DbSet<T> and dropped the rest. The temptation to
    recreate the full surface is real; resist it.
  • The Angular ABP UI. Our frontend is React, so this didn’t bite
    us — but if your UI is a tightly coupled ABP Angular template, the
    Granit React companion is a separate migration project, not a
    drop-in.

The bottom line

Ten weeks, 15 modules, zero downtime, ~40% reduction in module project
count, and a codebase where new hires don’t ask “why is there a
parallel abstraction on top of the BCL one?” anymore.

The full step-by-step is in the
Granit migration guide —
including every cross-cutting concern, the FAQ, and the strategy table
for which modules to port in which order.

If you’re sitting on an ABP codebase and wondering whether the move is
worth it, the honest answer is: only if Granit’s opinions match your
team’s
. Minimal API over auto-generated controllers, Wolverine over
generic event bus, BCL primitives over wrapped ones, Guid keys
everywhere, and Apache-2.0 over LGPL-3.0. If those five trade-offs
sound like wins, the migration is mechanical. If any of them sound
like regressions, stay on ABP — both frameworks are good at what
they do.

More on Granit:

Hepsiburada 'Bayram Ettiren Fırsatlar' Kampanyasını Başlattı

Hepsiburada, bayram alışverişleri için moda, teknoloji, süpermarket ve ev yaşam kategorilerinde indirim, kupon ve peşin fiyatına 9 taksit fırsatı sunan ‘Bayram Ettiren Fırsatlar’ kampanyasını duyurdu. Kampanyada çikolata, lokum gibi ikramlıklarda yüzde 50’ye varan indirimler, süpermarkette sepette yüzde 15 indirime ek 150 TL kupon, modada yüzde 60’a varan indirimler ve seçili bilgisayarlarda yüzde 10 indirim gibi avantajlar yer alıyor.

Yapay Zeka Taklidiyle 92 Bin Kötü Amaçlı Yazılım Tespiti

Kaspersky, ocak-mayıs döneminde yapay zeka markalarını taklit eden 92 bin kötü amaçlı yazılım tespit etti. Saldırganlar, sahte Claude AI uygulamalarıyla kullanıcıları hedef alıyor. Şirket, güvenlik çözümleri kullanılmasını ve güvenilir yapay zeka hizmetlerinin tercih edilmesini öneriyor.